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@@ -0,0 +1,1604 @@
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+#!/usr/bin/env python
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+# coding: utf-8
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+
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+import time
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+import datetime
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+import numpy as np
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+import math
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+from korean_lunar_calendar import KoreanLunarCalendar
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+import configparser
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+import pymssql
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+
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+from sklearn import ensemble
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+from sklearn.model_selection import train_test_split
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+
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+
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+## Measure
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+def MAPE(y_observed, y_pred):
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+ return np.mean(np.abs((y_observed - y_pred) / y_observed)) * 100
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+def MAE(y_observed, y_pred):
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+ return np.mean(np.abs(y_observed - y_pred))
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+def MBE(y_observed, y_pred):
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+ return (np.sum((y_observed - y_pred))/(len(y_observed)*np.mean(y_observed)))*100
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+def CVRMSE(y_observed, y_pred):
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+ return (np.sqrt(np.mean((y_observed - y_pred)*(y_observed - y_pred)))/np.mean(y_observed))*100
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+
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+
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+def Check_AlivedTimeStamp(RawData, ComparedData, idx_raw, idx_comp, unit):
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+ if unit == 'daily':
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+ if datetime.date(RawData[idx_raw].year, RawData[idx_raw].month, RawData[idx_raw].day) == datetime.date(ComparedData[idx_comp].year, ComparedData[idx_comp].month, ComparedData[idx_comp].day):
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+ isAlived = True
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+ else:
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+ isAlived = False
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+ elif unit == 'quarterly':
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+ if datetime.datetime(RawData[idx_raw][4].year,RawData[idx_raw][4].month,RawData[idx_raw][4].day,RawData[idx_raw][4].hour,RawData[idx_raw][4].minute) == datetime.datetime(ComparedData[idx_comp].year, ComparedData[idx_comp].month, ComparedData[idx_comp].day,ComparedData[idx_comp].hour, ComparedData[idx_comp].minute):
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+ isAlived = True
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+ else:
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+ isAlived = False
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+ return isAlived
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+
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+def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday, Day_Period, OrgDataRes, isRecent):
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+ CumTime = datetime.datetime(int(startday.strftime('%Y')), int(startday.strftime('%m')), int(startday.strftime('%d')), 0, 0, 0)
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+ StandardTimeStamp_DayUnit = [CumTime]
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+ StandardTimeStamp_QuarterUnit = [CumTime]
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+ # Create intact time stamp
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+ for idx_day in range(Day_Period):
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+ StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
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+ if isRecent and idx_day == Day_Period-1:
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+ tmp_len = now.hour*4 + int(now.minute/15)
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+ for idx_time in range(tmp_len):
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+ CumTime += datetime.timedelta(minutes = 15)
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+ StandardTimeStamp_QuarterUnit.append(CumTime)
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+ else:
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+ for idx_time in range(OrgDataRes):
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+ CumTime += datetime.timedelta(minutes = 15)
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+ StandardTimeStamp_QuarterUnit.append(CumTime)
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+
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+
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+ ### Extract data within day period
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+ Raw_Date=[] # raw data (date)
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+ Raw_Value=[] # raw data (value)
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+ for i in range(len(raw_Data)):
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday:
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+ Raw_Date.append(raw_Data[i][4])
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+ Raw_Value.append(raw_Data[i][5])
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday:
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+ break
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+
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+ Data_len=len(Raw_Date)
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+ if isRecent:
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+ DataAct_len = (Day_Period-1)*OrgDataRes + now.hour*4 + int(now.minute/15)+1
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+ else:
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+ DataAct_len = Day_Period*OrgDataRes
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+
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+ ### Unknown/duplicated data counts
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+ DataCount=[]
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+ for i in range(len(StandardTimeStamp_DayUnit)):
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+ cnt_unk=0 # Unknown data count
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+ for j in range(Data_len-1):
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+ if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
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+ cnt_unk += 1
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+ if isRecent and i==len(StandardTimeStamp_DayUnit)-1:
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+ DataCount.append([StandardTimeStamp_DayUnit[i], now.hour*4 + int(now.minute/15) - cnt_unk])
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+ else:
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+ DataCount.append([StandardTimeStamp_DayUnit[i], OrgDataRes-cnt_unk])
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+
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+ DataCountMat=np.matrix(DataCount)
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+
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+ ######## 현재 DB 특성상 값이 중복되거나 시간테이블의 행 자체가 없는 경우가 있고, 이 데이터 1 step 앞뒤로 데이터가 비정상일 확률이 높으므로 비정상데이터 뿐만 아니라 앞뒤 1 step까지 NaN으로 처리함
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+ data_w_nan=[]
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+ idx=0
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+ idx2=0
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+ isBadData = False
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+ for i in range(DataAct_len):
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+ if datetime.date(raw_Data[idx][4].year,raw_Data[idx][4].month,raw_Data[idx][4].day) >= startday and datetime.date(raw_Data[idx][4].year,raw_Data[idx][4].month,raw_Data[idx][4].day) <= lastday:
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+ if isBadData == True:
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+ data_w_nan.append(np.nan)
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+ isBadData=False
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+ elif Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx, idx2, 'quarterly'):
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+ data_w_nan.append(raw_Data[idx][5])
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+ else:
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+ if i > 1:
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+ data_w_nan[-1]=np.nan
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+ data_w_nan.append(np.nan)
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+ #data_w_nan.append(np.nan)
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+ if raw_Data[idx+1][5] > 0 and Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx+1, idx2+1, 'quarterly'):
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+ isBadData = True
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+ idx -= 1
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+ idx2 += 1
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+ idx += 1
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+ return StandardTimeStamp_QuarterUnit, data_w_nan, DataCountMat
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+
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+
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+### 예보데이터는 내일 데이터까지 확보해야하기때문에 리스트 수가 설비 데이터에 비해 하루 치가 더 많다
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+def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday, lastday, Day_Period):
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+ StandardTimeStamp_DayUnit = []
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+ # Create intact time stamp
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+ for idx_day in range(Day_Period+1):
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+ StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
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+
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+ ### Extract data within day period
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+ Raw_Value_max=[] # raw data (value)
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+ Raw_Value_min=[]
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+ Raw_Value_mean=[]
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+ Raw_Date=[] # raw data (date)
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+ tmp_data=[raw_Data[0][5]]
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+ for i in range(len(raw_Data)):
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+ if i == len(raw_Data)-1:
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+ Raw_Date.append(raw_Data[i][4])
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+ Raw_Value_max.append(max(tmp_data))
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+ Raw_Value_min.append(min(tmp_data))
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+ Raw_Value_mean.append(np.mean(tmp_data))
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+ elif datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday + datetime.timedelta(days=1):
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) != datetime.date(raw_Data[i+1][4].year,raw_Data[i+1][4].month,raw_Data[i+1][4].day):
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+ Raw_Date.append(raw_Data[i][4])
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+ Raw_Value_max.append(max(tmp_data))
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+ Raw_Value_min.append(min(tmp_data))
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+ Raw_Value_mean.append(np.mean(tmp_data))
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+ tmp_data=[]
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+ tmp_data.append(raw_Data[i+1][5])
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday + datetime.timedelta(days=1):
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+ break
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+
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+ Data_len=len(Raw_Date)
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+ ### Unknown/duplicated data counts
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+ DataCount=[]
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+ for i in range(len(StandardTimeStamp_DayUnit)):
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+ cnt_unk=0 # Unknown data count
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+ for j in range(Data_len-1):
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+ if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
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+ cnt_unk += 1
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+ DataCount.append([StandardTimeStamp_DayUnit[i], 1-cnt_unk])
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+ DataCountMat=np.matrix(DataCount)
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+ ######## 현재 DB 특성상 값이 중복되거나 시간테이블의 행 자체가 없는 경우가 있고, 이 데이터 1 step 앞뒤로 데이터가 비정상일 확률이 높으므로 비정상데이터 뿐만 아니라 앞뒤 1 step까지 NaN으로 처리함
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+
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+ MaxData_w_nan=[]
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+ MinData_w_nan=[]
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+ MeanData_w_nan=[]
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+ for i in range(len(StandardTimeStamp_DayUnit)):
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+ for j in range(len(Raw_Date)):
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+ if Check_AlivedTimeStamp(Raw_Date, StandardTimeStamp_DayUnit, j, i, 'daily'):
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+ MaxData_w_nan.append(Raw_Value_max[j])
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+ MinData_w_nan.append(Raw_Value_min[j])
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+ MeanData_w_nan.append(Raw_Value_mean[j])
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+ break
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+ elif j == len(Raw_Date)-1:
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+ MaxData_w_nan.append(np.nan)
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+ MinData_w_nan.append(np.nan)
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+ MeanData_w_nan.append(np.nan)
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+
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+ return StandardTimeStamp_DayUnit, MaxData_w_nan, MinData_w_nan, MeanData_w_nan, DataCountMat
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+
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+
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+### Define day-type
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+def getDayName(year, month, day):
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+ return ['MON','TUE','WED','THU','FRI','SAT','SUN'][datetime.date(year, month, day).weekday()]
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+def getDayType(DateinDay, Period, SpecialHoliday):
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+ DoW=[]; # Day of Week
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+ for i in range(Period):
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+ if DateinDay[i].year==2019 and DateinDay[i].month==5 and DateinDay[i].day==18:
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+ DoW.append([5, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'MON':
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+ DoW.append([1, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'TUE':
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+ DoW.append([2, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'WED':
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+ DoW.append([3, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'THU':
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+ DoW.append([4, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'FRI':
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+ DoW.append([5, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SAT':
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+ DoW.append([6, DateinDay[i]])
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+ elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SUN':
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+ DoW.append([7, DateinDay[i]])
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+
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+ for j in range(len(SpecialHoliday)):
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+ if SpecialHoliday[j] == datetime.date(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day):
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+ DoW[-1][0] = 8
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+ break
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+
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+ ### W-W:1, N-W:2, W-N:3, N-N:4 ###
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+ DayType=[]
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+ for i in range(Period):
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+ if i==0:
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+ if DoW[i][0] <= 5:
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+ DayType.append([1, DateinDay[i]])
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+ elif DoW[i][0] > 5:
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+ DayType.append([3, DateinDay[i]])
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+ else:
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+ if DoW[i-1][0] <= 5 and DoW[i][0] <= 5:
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+ DayType.append([1, DateinDay[i]])
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+ elif DoW[i-1][0] > 5 and DoW[i][0] <= 5:
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+ DayType.append([2, DateinDay[i]])
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+ elif DoW[i-1][0] <= 5 and DoW[i][0] > 5:
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+ DayType.append([3, DateinDay[i]])
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+ elif DoW[i-1][0] > 5 and DoW[i][0] > 5:
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+ DayType.append([4, DateinDay[i]])
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+ return DoW, DayType
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+
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+
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+if __name__ == "__main__" :
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+ Init = True
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+ ## Check every 15min. in the infinite loop
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+ while True:
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+ now = datetime.datetime.now().now()
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+ ## distinguish real time update and specific day
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+ ## 자정에 생기는 인덱싱 문제로 0시에는 16분에 업데이트, 나머지는 15분에 한 번씩 업데이트
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+ if Init:
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+ prev_time_minute = now.minute - 1 ## 알고리즘 중복 수행 방지 (알고리즘 수행시 1분이 안걸리기에 한타임에 알고리즘 한번만 동작시키기 위함)
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+ if (now.hour != 0 and now.minute%15 == 1 and now.second > 0 and now.second < 5) and prev_time_minute != now.minute:
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+ ActiveAlgorithm = True
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+ prev_time_minute = now.minute
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+ else:
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+ ActiveAlgorithm = False
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+
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+ if ActiveAlgorithm or Init:
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+
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+ ## Loading .ini file
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+ myINI = configparser.ConfigParser()
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+ myINI.read("Config.ini", "utf-8" )
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+ # MSSQL Access
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+ conn = pymssql.connect(host=myINI.get('LocalDB_Info','ip_address'), user=myINI.get('LocalDB_Info','user_id'), password=myINI.get('LocalDB_Info','user_password'), database = myINI.get('LocalDB_Info','db_name'), autocommit=True)
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+ # Create Cursor from Connection
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+ cursor = conn.cursor()
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+
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+ # Execute SQL (Electric consumption)
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+ cursor.execute('SELECT * FROM BemsConfigData where SiteId = 1')
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+
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+ rowDB_info = cursor.fetchone()
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+
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+ conn.close()
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+
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+ loadDBIP = rowDB_info[1]
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+ loadDBUserID = rowDB_info[2]
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+ loadDBUserPW = rowDB_info[3]
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+ loadDBName = rowDB_info[4]
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+ targetDBIP = rowDB_info[5]
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+ targetDBUserID = rowDB_info[6]
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+ targetDBUserPW = rowDB_info[7]
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+ targetDBName = rowDB_info[8]
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+
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+ startday = datetime.date(int(rowDB_info[9].year), int(rowDB_info[9].month), int(rowDB_info[9].day))
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+
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+ now=datetime.datetime.now().now()
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+ lastday = datetime.date(now.year, now.month, now.day)
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+ isRecent = True
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+
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+ if startday < datetime.date(2020,4,8):
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+ print('[ERROR] 데이터 최소 시작 시점은 2020.04.08 입니다')
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+ startday = datetime.date(2020,4,9)
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+ elif startday > lastday:
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+ print('[ERROR] 예측 타깃 시작시점이 데이터 시작 시점보다 작을 수 없습니다')
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+
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+
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+ ##############################################################################################
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+ ## 기온, 습도 예보 데이터 로드
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+ # MSSQL 접속
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+ conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
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+ # Connection 으로부터 Cursor 생성
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+ cursor = conn.cursor()
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+
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+ # SQL문 실행 (기온 예보)
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+ cursor.execute('SELECT * FROM BemsMonitoringPointWeatherForecasted where SiteId = 1 and Category = '+"'"+'Temperature'+"'"+' order by ForecastedDateTime desc')
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+
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+ row = cursor.fetchone()
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+ rawWFTemperature = [row]
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+ while row:
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+ row = cursor.fetchone()
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+ if row == None:
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+ break
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+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
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+ break
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+ rawWFTemperature.append(row)
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+ rawWFTemperature.reverse()
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+
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+ # SQL문 실행 (습도 예보)
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+ cursor.execute('SELECT * FROM BemsMonitoringPointWeatherForecasted where SiteId = 1 and Category = '+"'"+'Humidity'+"'"+' order by ForecastedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawWFHumidity = [row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawWFHumidity.append(row)
|
|
|
+ rawWFHumidity.reverse()
|
|
|
+ ##############################################################################################
|
|
|
+
|
|
|
+ startday = datetime.date(rawWFHumidity[0][4].year, rawWFHumidity[0][4].month, rawWFHumidity[0][4].day) ## 데이터 불러오는 DB가 선구축된다고 가정하여 예보데이터 기준으로 startday define
|
|
|
+ DayPeriod = (lastday - startday).days + 1
|
|
|
+ print('* StartDay :',startday,',', 'LastDay :', lastday,',','Current Time :', now, ',','Day period :', DayPeriod)
|
|
|
+
|
|
|
+
|
|
|
+ # MSSQL 접속
|
|
|
+ conn = pymssql.connect(host = loadDBIP, user = loadDBUserID, password = loadDBUserPW, database = loadDBName, autocommit=True)
|
|
|
+
|
|
|
+ # Connection 으로부터 Cursor 생성
|
|
|
+ cursor = conn.cursor()
|
|
|
+
|
|
|
+ DataRes_96=96
|
|
|
+ DataRes_24=24
|
|
|
+
|
|
|
+ print('************ (Start) Load & pre-processing data !! ************')
|
|
|
+ # SQL문 실행 (축열조 축열량)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawChillerCalAmount=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawChillerCalAmount.append(row)
|
|
|
+ rawChillerCalAmount.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (축열조 제빙운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 16 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawChillerStatusIcing=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawChillerStatusIcing.append(row)
|
|
|
+ rawChillerStatusIcing.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (축열조 축단운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 17 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawChillerStatusDeicing=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawChillerStatusDeicing.append(row)
|
|
|
+ rawChillerStatusDeicing.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (축열조 병렬운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 18 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawChillerStatusParallel=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawChillerStatusParallel.append(row)
|
|
|
+ rawChillerStatusParallel.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (축열조 냉단운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 19 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawChillerStatusRefOnly=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawChillerStatusRefOnly.append(row)
|
|
|
+ rawChillerStatusRefOnly.reverse()
|
|
|
+
|
|
|
+
|
|
|
+ ## 현재 2019, 2020년 냉동기 전력량 데이터가 없으므로 2년 전 데이터를 활용
|
|
|
+ # SQL문 실행 (냉동기1 전력량)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefPowerConsume1=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawRefPowerConsume1.append(row)
|
|
|
+ rawRefPowerConsume1.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기1 운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefStatus1=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawRefStatus1.append(row)
|
|
|
+ rawRefStatus1.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기2 전력량)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefPowerConsume2=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawRefPowerConsume2.append(row)
|
|
|
+ rawRefPowerConsume2.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기2 운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefStatus2=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawRefStatus2.append(row)
|
|
|
+ rawRefStatus2.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (브라인 입구온도)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 4 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawBrineInletTemperature=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawBrineInletTemperature.append(row)
|
|
|
+ rawBrineInletTemperature.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (브라인 출구온도)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 3 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawBrineOutletTemperature=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawBrineOutletTemperature.append(row)
|
|
|
+ rawBrineOutletTemperature.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (브라인 혼합온도)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 22 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawBrineMixedTemperature=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawBrineMixedTemperature.append(row)
|
|
|
+ rawBrineMixedTemperature.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (브라인 통과유량)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 5 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawBrineFlowAmount=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
|
|
|
+ break
|
|
|
+ rawBrineFlowAmount.append(row)
|
|
|
+ rawBrineFlowAmount.reverse()
|
|
|
+
|
|
|
+
|
|
|
+ # SQL문 실행 (정기휴일)
|
|
|
+ cursor.execute('SELECT * FROM CmHoliday where SiteId = 1 and IsUse = 1')
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ regularHolidayData = [row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ regularHolidayData.append(row)
|
|
|
+ regularHolidayData = regularHolidayData[0:-1]
|
|
|
+
|
|
|
+ # SQL문 실행 (비정기휴일)
|
|
|
+ cursor.execute('SELECT * FROM CmHolidayCustom where SiteId = 1 and IsUse = 1')
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ suddenHolidayData = [row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ suddenHolidayData.append(row)
|
|
|
+ suddenHolidayData = suddenHolidayData[0:-1]
|
|
|
+
|
|
|
+ ##############################################################################################
|
|
|
+ ## 현재 2019, 2020년 냉동기 전력량 데이터가 없으므로 2년 전 데이터를 활용
|
|
|
+ # SQL문 실행 (냉동기1 전력량), 2018
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefPowerConsume1_2018=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
|
|
|
+ break
|
|
|
+ rawRefPowerConsume1_2018.append(row)
|
|
|
+ rawRefPowerConsume1_2018.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기1 운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefStatus1_2018=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
|
|
|
+ break
|
|
|
+ rawRefStatus1_2018.append(row)
|
|
|
+ rawRefStatus1_2018.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기2 전력량)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefPowerConsume2_2018=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
|
|
|
+ break
|
|
|
+ rawRefPowerConsume2_2018.append(row)
|
|
|
+ rawRefPowerConsume2_2018.reverse()
|
|
|
+
|
|
|
+ # SQL문 실행 (냉동기2 운전상태)
|
|
|
+ cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawRefStatus2_2018=[row]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row == None:
|
|
|
+ break
|
|
|
+ if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
|
|
|
+ break
|
|
|
+ rawRefStatus2_2018.append(row)
|
|
|
+ rawRefStatus2_2018.reverse()
|
|
|
+
|
|
|
+ ##############################################################################################
|
|
|
+
|
|
|
+ # 연결 끊기
|
|
|
+ conn.close()
|
|
|
+
|
|
|
+ ## 휴일 데이터 DB에서 호출
|
|
|
+
|
|
|
+ # 공휴일의 음력 계산
|
|
|
+ calendar_convert = KoreanLunarCalendar()
|
|
|
+ SpecialHoliday = []
|
|
|
+ for i in range(lastday.year-startday.year+1):
|
|
|
+ for j in range(len(regularHolidayData)):
|
|
|
+ if regularHolidayData[j][3] == 1:
|
|
|
+ if regularHolidayData[j][1] == 12 and regularHolidayData[j][2] == 30: ## 설 하루 전 연휴 계산을 위함
|
|
|
+ calendar_convert.setLunarDate(startday.year+i-1, regularHolidayData[j][1], regularHolidayData[j][2], False)
|
|
|
+ SpecialHoliday.append(datetime.date(int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[0]), int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[1]), int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[2])))
|
|
|
+ else:
|
|
|
+ calendar_convert.setLunarDate(startday.year+i, regularHolidayData[j][1], regularHolidayData[j][2], False)
|
|
|
+ SpecialHoliday.append(datetime.date(int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[0]), int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[1]), int(calendar_convert.SolarIsoFormat().split(' ')[0].split('-')[2])))
|
|
|
+ else:
|
|
|
+ SpecialHoliday.append(datetime.date(startday.year+i,regularHolidayData[j][1],regularHolidayData[j][2]))
|
|
|
+
|
|
|
+ for i in range(len(suddenHolidayData)):
|
|
|
+ if suddenHolidayData[i][1].year >= startday.year:
|
|
|
+ SpecialHoliday.append(datetime.date(suddenHolidayData[i][1].year, suddenHolidayData[i][1].month, suddenHolidayData[i][1].day))
|
|
|
+
|
|
|
+ SpecialHoliday=list(set(SpecialHoliday))
|
|
|
+
|
|
|
+ ##############################################################################################
|
|
|
+
|
|
|
+ ChillerCalAmount_Date, ChillerCalAmount_w_nan, DataCountMat_ChillerCalAmount = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerCalAmount, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ BrineMixedTemperature_Date, BrineMixedTemperature_w_nan, DataCountMat_BrineMixedTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineMixedTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ BrineInletTemperature_Date, BrineInletTemperature_w_nan, DataCountMat_BrineInletTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineInletTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ BrineOutletTemperature_Date, BrineOutletTemperature_w_nan, DataCountMat_BrineOutletTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineOutletTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ BrineFlowAmount_Date, BrineFlowAmount_w_nan, DataCountMat_BrineFlowAmount = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineFlowAmount, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ ChStatusIcing_Date, ChStatusIcing_w_nan, DataCountMat_ChStatusIcing = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusIcing, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ ChStatusDeicing_Date, ChStatusDeicing_w_nan, DataCountMat_ChStatusDeicing = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusDeicing, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ ChStatusParallel_Date, ChStatusParallel_w_nan, DataCountMat_ChStatusParallel = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusParallel, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ ChStatusRefOnly_Date, ChStatusRefOnly_w_nan, DataCountMat_ChStatusRefOnly = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusRefOnly, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ RefPowerConsume1_Date, RefPowerConsume1_w_nan, DataCountMat_RefPowerConsume1 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume1, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ RefPowerConsume2_Date, RefPowerConsume2_w_nan, DataCountMat_RefPowerConsume2 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume2, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ RefStatus1_Date, RefStatus1_w_nan, DataCountMat_RefStatus1 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus1, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+ RefStatus2_Date, RefStatus2_w_nan, DataCountMat_RefStatus2 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus2, startday, lastday, DayPeriod, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ ##############################################################################################
|
|
|
+ ## 2019, 2020년 냉동기 전력량이 없어서 2018년 데이터로 대체
|
|
|
+ DayPeriod_2018 = (datetime.date(2018,12,31) - datetime.date(2018,1,1)).days + 1
|
|
|
+
|
|
|
+ RefPowerConsume1_2018_Date, RefPowerConsume1_2018_w_nan, DataCountMat_RefPowerConsume1_2018 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume1_2018, datetime.date(2018,1,1), datetime.date(2018,12,31), DayPeriod_2018, DataRes_96, isRecent)
|
|
|
+ RefPowerConsume2_2018_Date, RefPowerConsume2_2018_w_nan, DataCountMat_RefPowerConsume2_2018 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume2_2018, datetime.date(2018,1,1), datetime.date(2018,12,31), DayPeriod_2018, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ RefStatus1_Date_2018, RefStatus1_2018_w_nan, DataCountMat_RefStatus1_2018 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus1_2018, datetime.date(2018,1,1), datetime.date(2018,12,31), DayPeriod_2018, DataRes_96, isRecent)
|
|
|
+ RefStatus2_2018_Date, RefStatus2_2018_w_nan, DataCountMat_RefStatus2_2018 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus2_2018, datetime.date(2018,1,1), datetime.date(2018,12,31), DayPeriod_2018, DataRes_96, isRecent)
|
|
|
+
|
|
|
+ ################# Using the power Consumption of Refrigerator in 2018 instead of 2020 #################
|
|
|
+ #### 전력 소비량 계산
|
|
|
+ _st=90*96
|
|
|
+ _end=195*96
|
|
|
+ period_2018=(_end-_st)/96
|
|
|
+ RefStatus1_2018_w_nan_tmp=RefStatus1_2018_w_nan[_st:_end]
|
|
|
+ RefPowerConsume1_2018_w_nan_tmp=RefPowerConsume1_2018_w_nan[_st:_end]
|
|
|
+
|
|
|
+ RefStatus2_2018_w_nan_tmp=RefStatus2_2018_w_nan[_st:_end]
|
|
|
+ RefPowerConsume2_2018_w_nan_tmp=RefPowerConsume2_2018_w_nan[_st:_end]
|
|
|
+
|
|
|
+ ### Estimation based on Statistical method
|
|
|
+ X1 = []
|
|
|
+ X2 = []
|
|
|
+ Y1 = []
|
|
|
+ Y2 = []
|
|
|
+ TermNum = 96
|
|
|
+ for i in range(TermNum, len(RefStatus1_2018_w_nan_tmp),TermNum):
|
|
|
+ X1.append(RefStatus1_2018_w_nan_tmp[i-TermNum:i])
|
|
|
+ X2.append(RefStatus2_2018_w_nan_tmp[i-TermNum:i])
|
|
|
+ Y1.append(RefPowerConsume1_2018_w_nan_tmp[i-TermNum:i])
|
|
|
+ Y2.append(RefPowerConsume2_2018_w_nan_tmp[i-TermNum:i])
|
|
|
+
|
|
|
+ xTrain1, xTest1, yTrain1, yTest1 = train_test_split(X1, Y1, test_size=0.1, shuffle =False)
|
|
|
+ xTrain2, xTest2, yTrain2, yTest2 = train_test_split(X2, Y2, test_size=0.1, shuffle =False)
|
|
|
+
|
|
|
+ Y_tmp1=[]
|
|
|
+ Y_tmp2=[]
|
|
|
+ for i in range(len(xTrain1)):
|
|
|
+ for j in range(TermNum):
|
|
|
+ if xTrain1[i][j] == 1:
|
|
|
+ Y_tmp1.append(yTrain1[i][j])
|
|
|
+ if xTrain2[i][j] == 1:
|
|
|
+ Y_tmp2.append(yTrain2[i][j])
|
|
|
+
|
|
|
+ mean_RefConsume1=np.mean(Y_tmp1) # 냉동기1 전력량 평균
|
|
|
+ mean_RefConsume2=np.mean(Y_tmp2) # 냉동기2 전력량 평균
|
|
|
+
|
|
|
+ ##############################################################################################
|
|
|
+ ##############################################################################################
|
|
|
+
|
|
|
+ WFTemperature_Date, WFTemperatureMax_w_nan, WFTemperatureMin_w_nan, WFTemperatureMean_w_nan, DataCountMat_WFTemperature = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFTemperature, startday, lastday, DayPeriod)
|
|
|
+ WFHumidity_Date, WFHumidityMax_w_nan, WFHumidityMin_w_nan, WFHumidityMean_w_nan, DataCountMat_WFHumidity = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFHumidity, startday, lastday, DayPeriod)
|
|
|
+
|
|
|
+ RawDate = ChillerCalAmount_Date
|
|
|
+
|
|
|
+ ## 축열조 상태 변수 - 제빙운전:10, 축단운전:20, 병렬운전:30, 냉단운전:40, OFF:0
|
|
|
+ Icing=10
|
|
|
+ StorageOnly=20
|
|
|
+ Parallel=30
|
|
|
+ ChillerOnly=40
|
|
|
+ Off=0
|
|
|
+ ChillerStatus=[]
|
|
|
+ for i in range(len(ChStatusIcing_Date)):
|
|
|
+ if ChStatusIcing_w_nan[i]==1:
|
|
|
+ ChillerStatus.append(Icing)
|
|
|
+ elif ChStatusDeicing_w_nan[i]==1:
|
|
|
+ ChillerStatus.append(StorageOnly)
|
|
|
+ elif ChStatusParallel_w_nan[i]==1:
|
|
|
+ ChillerStatus.append(Parallel)
|
|
|
+ elif ChStatusRefOnly_w_nan[i]==1:
|
|
|
+ ChillerStatus.append(ChillerOnly)
|
|
|
+ elif ChStatusIcing_w_nan[i]==0 or ChStatusDeicing_w_nan[i]==0 or ChStatusParallel_w_nan[i]==0 or ChStatusRefOnly_w_nan[i]==0:
|
|
|
+ ChillerStatus.append(Off)
|
|
|
+ else:
|
|
|
+ ChillerStatus.append(np.nan)
|
|
|
+
|
|
|
+ ## 축/방열량에 대해서 두가지 변수를 생성한다.
|
|
|
+ ## 첫번쨰는 사용자에게 상대적 열량을 보여주기 위해 0 < Q < max(Q) 사이의 값으로 구성된 열량
|
|
|
+ ## 두번쨰는 실질적 계산을 위해서 NaN이 포함된 날은 제외하고 학습하므로 NaN 구간의 축/방열량은 0으로 가정하고 산출
|
|
|
+ ## 축적 열량의 최대치 (정격용량) = 3060 USRT (=10,924.2 kW)일 때 100%
|
|
|
+ max_q_accum_kWh = 3060*3.57
|
|
|
+ q_accum_kWh=[0]
|
|
|
+ nan_cnt=0
|
|
|
+ nan_point=[]
|
|
|
+ for i in range(len(ChillerStatus)):
|
|
|
+ if math.isnan(ChillerStatus[i]): # Nan의 경우 축열량을 0이라고 가정하고 진행
|
|
|
+ q_accum_kWh.append(q_accum_kWh[-1])
|
|
|
+ nan_cnt += 1
|
|
|
+ nan_point.append(i)
|
|
|
+ else:
|
|
|
+ if ChillerStatus[i] == Icing and BrineInletTemperature_w_nan[i] < BrineMixedTemperature_w_nan[i]:
|
|
|
+ q_accum_kWh.append(q_accum_kWh[-1] + (BrineFlowAmount_w_nan[i]*0.06*1.042*3.14*0.28*0.25)*(BrineMixedTemperature_w_nan[i]-BrineInletTemperature_w_nan[i]))
|
|
|
+ elif ChillerStatus[i] == StorageOnly and BrineInletTemperature_w_nan[i] > BrineMixedTemperature_w_nan[i]:
|
|
|
+ q_accum_kWh.append(q_accum_kWh[-1] - (BrineFlowAmount_w_nan[i]*0.06*1.042*3.14*0.28*0.25)*(BrineInletTemperature_w_nan[i]-BrineMixedTemperature_w_nan[i]))
|
|
|
+ elif ChillerStatus[i] == Parallel and BrineInletTemperature_w_nan[i] > BrineMixedTemperature_w_nan[i]:
|
|
|
+ q_accum_kWh.append(q_accum_kWh[-1] - (BrineFlowAmount_w_nan[i]*0.06*1.042*3.14*0.28*0.25)*(BrineInletTemperature_w_nan[i]-BrineMixedTemperature_w_nan[i]))
|
|
|
+ else: #ChillerStatus[i] == Off or ChillerStatus[i] == ChillerOnly:
|
|
|
+ q_accum_kWh.append(q_accum_kWh[-1])
|
|
|
+
|
|
|
+ if q_accum_kWh[-1] < 0:
|
|
|
+ q_accum_kWh[-1] = 0
|
|
|
+ elif q_accum_kWh[-1] > max_q_accum_kWh:
|
|
|
+ q_accum_kWh[-1] = max_q_accum_kWh
|
|
|
+
|
|
|
+ if nan_cnt > 48:
|
|
|
+ print('[Warning] Too many nan points exist (48 points sequentially)')
|
|
|
+ nan_cnt = 0
|
|
|
+
|
|
|
+ q_accum_kWh = q_accum_kWh[1:len(q_accum_kWh)]
|
|
|
+ q_accum_percent=[]
|
|
|
+ for i in range(len(q_accum_kWh)):
|
|
|
+ q_accum_percent.append((q_accum_kWh[i]/max_q_accum_kWh)*100)
|
|
|
+
|
|
|
+ CalAmount_prev = q_accum_percent[:len(q_accum_percent)-96] ## DB에 비어있는 이전 축열량이 있다면 채워주기 위함
|
|
|
+
|
|
|
+ #################### Calculate the Gradient on Each Operation Mode ########################
|
|
|
+ cnt_nan=0
|
|
|
+ CalAmount_wo_nan=[]
|
|
|
+ ChillerStatus_wo_nan=[]
|
|
|
+ RefStatus1_wo_nan=[]
|
|
|
+ RefStatus2_wo_nan=[]
|
|
|
+ RefStatus_wo_nan=[]
|
|
|
+
|
|
|
+ ## 1: off,off, 2: on,off, 3: on,on
|
|
|
+ for i in range(len(q_accum_percent)):
|
|
|
+ if not np.isnan(q_accum_percent[i]) and not np.isnan(ChillerStatus[i]) and not np.isnan(RefStatus1_w_nan[i]) and not np.isnan(RefStatus2_w_nan[i]):
|
|
|
+ CalAmount_wo_nan.append(q_accum_percent[i])
|
|
|
+ ChillerStatus_wo_nan.append(ChillerStatus[i])
|
|
|
+ RefStatus1_wo_nan.append(RefStatus1_w_nan[i])
|
|
|
+ RefStatus2_wo_nan.append(RefStatus2_w_nan[i])
|
|
|
+ RefStatus_wo_nan.append(RefStatus1_w_nan[i]+RefStatus2_w_nan[i])
|
|
|
+ cnt_nan=0
|
|
|
+ else:
|
|
|
+ CalAmount_wo_nan.append(CalAmount_wo_nan[-1])
|
|
|
+ ChillerStatus_wo_nan.append(0)
|
|
|
+ RefStatus1_wo_nan.append(0)
|
|
|
+ RefStatus2_wo_nan.append(0)
|
|
|
+ RefStatus_wo_nan.append(0)
|
|
|
+ cnt_nan+=1
|
|
|
+ if cnt_nan>12:
|
|
|
+ cnt_nan=0
|
|
|
+ # print('There are many unknown data!')
|
|
|
+
|
|
|
+ # 학습용 데이터로 사용
|
|
|
+ train_size = int(len(ChillerStatus_wo_nan))
|
|
|
+ ## 나머지를 검증용 데이터로 사용
|
|
|
+ ## test_size = len(ChillerStatus_wo_nan) - train_size
|
|
|
+
|
|
|
+ trainStatus = np.array(ChillerStatus_wo_nan[0:train_size])
|
|
|
+ trainCalAmount = np.array(CalAmount_wo_nan[0:train_size])
|
|
|
+ trainRefStatus1 = np.array(RefStatus1_wo_nan[0:train_size])
|
|
|
+ trainRefStatus2 = np.array(RefStatus2_wo_nan[0:train_size])
|
|
|
+
|
|
|
+
|
|
|
+ GradientCalAmount_mode_Icing = []
|
|
|
+ GradientCalAmount_mode_StorageOnly = []
|
|
|
+ GradientCalAmount_mode_Parallel = []
|
|
|
+ GradientCalAmount_mode_ChillerOnly = []
|
|
|
+ isNan_Point = False
|
|
|
+ for i in range(len(trainStatus)):
|
|
|
+ for j in range(len(nan_point)):
|
|
|
+ if i == nan_point[j]:
|
|
|
+ isNan_Point=True
|
|
|
+ break
|
|
|
+ if not isNan_Point:
|
|
|
+ if trainStatus[i] == Icing and trainCalAmount[i] > trainCalAmount[i-1] and trainRefStatus1[i] == 1 and trainRefStatus2[i] == 1:
|
|
|
+ GradientCalAmount_mode_Icing.append(trainCalAmount[i]-trainCalAmount[i-1])
|
|
|
+ elif trainStatus[i] == StorageOnly and trainCalAmount[i] < trainCalAmount[i-1]:
|
|
|
+ GradientCalAmount_mode_StorageOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
|
|
|
+ elif trainStatus[i] == Parallel and trainCalAmount[i] < trainCalAmount[i-1] and (trainRefStatus1[i] == 1 or trainRefStatus2[i] == 1):
|
|
|
+ GradientCalAmount_mode_Parallel.append(trainCalAmount[i]-trainCalAmount[i-1])
|
|
|
+ elif trainStatus[i] == ChillerOnly and trainRefStatus1[i] == 1 and trainRefStatus2[i] == 1:
|
|
|
+ GradientCalAmount_mode_ChillerOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
|
|
|
+ isNan_Point = False
|
|
|
+
|
|
|
+ GradientCalAmount_w3sigma_mode_Icing = []
|
|
|
+ if len(GradientCalAmount_mode_Icing) != 0:
|
|
|
+ max3sigma_mode_Icing = np.mean(GradientCalAmount_mode_Icing)+np.std(GradientCalAmount_mode_Icing)*3
|
|
|
+ min3sigma_mode_Icing = np.mean(GradientCalAmount_mode_Icing)-np.std(GradientCalAmount_mode_Icing)*3
|
|
|
+
|
|
|
+ GradientCalAmount_w3sigma_mode_StorageOnly = []
|
|
|
+ if len(GradientCalAmount_mode_StorageOnly) != 0:
|
|
|
+ max3sigma_mode_StorageOnly = np.mean(GradientCalAmount_mode_StorageOnly)+np.std(GradientCalAmount_mode_StorageOnly)*3
|
|
|
+ min3sigma_mode_StorageOnly = np.mean(GradientCalAmount_mode_StorageOnly)-np.std(GradientCalAmount_mode_StorageOnly)*3
|
|
|
+
|
|
|
+ GradientCalAmount_w3sigma_mode_Parallel = []
|
|
|
+ if len(GradientCalAmount_mode_Parallel) != 0:
|
|
|
+ max3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)+np.std(GradientCalAmount_mode_Parallel)*3
|
|
|
+ min3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)-np.std(GradientCalAmount_mode_Parallel)*3
|
|
|
+
|
|
|
+ GradientCalAmount_w3sigma_mode_ChillerOnly = []
|
|
|
+ if len(GradientCalAmount_mode_ChillerOnly) != 0:
|
|
|
+ max3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)+np.std(GradientCalAmount_mode_ChillerOnly)*3
|
|
|
+ min3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)-np.std(GradientCalAmount_mode_ChillerOnly)*3
|
|
|
+
|
|
|
+
|
|
|
+ for i in range(len(GradientCalAmount_mode_Icing)):
|
|
|
+ if GradientCalAmount_mode_Icing[i] <= max3sigma_mode_Icing and GradientCalAmount_mode_Icing[i] >= min3sigma_mode_Icing:
|
|
|
+ GradientCalAmount_w3sigma_mode_Icing.append(GradientCalAmount_mode_Icing[i])
|
|
|
+
|
|
|
+ for i in range(len(GradientCalAmount_mode_StorageOnly)):
|
|
|
+ if GradientCalAmount_mode_StorageOnly[i] <= max3sigma_mode_StorageOnly and GradientCalAmount_mode_StorageOnly[i] >= min3sigma_mode_StorageOnly:
|
|
|
+ GradientCalAmount_w3sigma_mode_StorageOnly.append(GradientCalAmount_mode_StorageOnly[i])
|
|
|
+
|
|
|
+ for i in range(len(GradientCalAmount_mode_Parallel)):
|
|
|
+ if GradientCalAmount_mode_Parallel[i] <= max3sigma_mode_Parallel and GradientCalAmount_mode_Parallel[i] >= min3sigma_mode_Parallel:
|
|
|
+ GradientCalAmount_w3sigma_mode_Parallel.append(GradientCalAmount_mode_Parallel[i])
|
|
|
+
|
|
|
+ for i in range(len(GradientCalAmount_mode_ChillerOnly)):
|
|
|
+ if GradientCalAmount_mode_ChillerOnly[i] <= max3sigma_mode_ChillerOnly and GradientCalAmount_mode_ChillerOnly[i] >= min3sigma_mode_ChillerOnly:
|
|
|
+ GradientCalAmount_w3sigma_mode_ChillerOnly.append(GradientCalAmount_mode_ChillerOnly[i])
|
|
|
+
|
|
|
+ #print(np.mean(GradientCalAmount_w3sigma_mode_Icing), np.mean(GradientCalAmount_w3sigma_mode_StorageOnly), np.mean(GradientCalAmount_w3sigma_mode_Parallel), np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
|
|
|
+ #print(np.std(GradientCalAmount_w3sigma_mode_Icing), np.std(GradientCalAmount_w3sigma_mode_StorageOnly), np.std(GradientCalAmount_w3sigma_mode_Parallel), np.std(GradientCalAmount_w3sigma_mode_ChillerOnly))
|
|
|
+
|
|
|
+ print('************ (Finish) Load & pre-processing data !! ************')
|
|
|
+ print('****************************************************************')
|
|
|
+ #######################################################################################
|
|
|
+
|
|
|
+
|
|
|
+ ############################################################################################################
|
|
|
+ #################### Prediction for the Degree of Daily Deicing ############################################
|
|
|
+ ## 매일 21시~21시 15분 사이에 산출 및 DB 삽입
|
|
|
+
|
|
|
+ if (now.hour == 21 and (now.minute > 0 or now.minute < 16)) or Init:
|
|
|
+
|
|
|
+ print('************ (Start) The Degree of Daily Deicing is being predicted!! ************')
|
|
|
+ DailyDeicingAmount = []
|
|
|
+ DailyDeicingAmount_kWh = []
|
|
|
+ idx = 0
|
|
|
+
|
|
|
+ if isRecent and now.hour < 21: ## 21시를 전, 후로 익일 예상 방냉량이 업데이트
|
|
|
+ _DayPeriod = DayPeriod-1
|
|
|
+ else:
|
|
|
+ _DayPeriod = DayPeriod
|
|
|
+ for i in range(_DayPeriod):
|
|
|
+ tmpAmount = []
|
|
|
+ tmpAmount_kWh = []
|
|
|
+
|
|
|
+ if i == 0:
|
|
|
+ time_length = 4*21 # 첫번째 날은 저녁 9시까지 방냉량만 산출
|
|
|
+ else:
|
|
|
+ time_length = 96
|
|
|
+ for time_idx in range(time_length):
|
|
|
+ if q_accum_percent[idx] > q_accum_percent[idx+1]:
|
|
|
+ tmpAmount.append(q_accum_percent[idx]-q_accum_percent[idx+1])
|
|
|
+ tmpAmount_kWh.append(q_accum_kWh[idx]-q_accum_kWh[idx+1])
|
|
|
+ idx += 1
|
|
|
+ if len(tmpAmount) > 0:
|
|
|
+ DailyDeicingAmount.append(sum(tmpAmount))
|
|
|
+ DailyDeicingAmount_kWh.append(sum(tmpAmount_kWh))
|
|
|
+ else:
|
|
|
+ DailyDeicingAmount.append(0)
|
|
|
+ DailyDeicingAmount_kWh.append(0)
|
|
|
+
|
|
|
+ DateinDay=[]
|
|
|
+ for k in range(_DayPeriod):
|
|
|
+ DateinDay.append(RawDate[k*DataRes_96])
|
|
|
+ DoW, DayType = getDayType(DateinDay, _DayPeriod, SpecialHoliday)
|
|
|
+
|
|
|
+ # Collect the normal data
|
|
|
+ X = []
|
|
|
+ Y = []
|
|
|
+ _isnan = False
|
|
|
+
|
|
|
+ for i in range(_DayPeriod):
|
|
|
+ if DayType[i][0] < 3 and DailyDeicingAmount[i] > 0: ## 평일이면서 축열조를 가동하고 결측값이 없는 날만 추출
|
|
|
+ if i == _DayPeriod-1:
|
|
|
+ time_len = int(len(ChillerStatus)%96)
|
|
|
+ else:
|
|
|
+ time_len = DataRes_96
|
|
|
+ for j in range(time_len):
|
|
|
+ if math.isnan(ChillerStatus[i*DataRes_96+j]):
|
|
|
+ _isnan = True
|
|
|
+ if not _isnan:
|
|
|
+ X.append([WFTemperatureMax_w_nan[i], WFTemperatureMin_w_nan[i], WFTemperatureMean_w_nan[i], WFHumidityMax_w_nan[i], WFHumidityMin_w_nan[i], WFHumidityMean_w_nan[i]])
|
|
|
+ Y.append(DailyDeicingAmount[i])
|
|
|
+ _isnan = False
|
|
|
+
|
|
|
+ xTrain, xVal, yTrain, yVal = train_test_split(X, Y, test_size=0.001, shuffle = False)
|
|
|
+ xTomorrow_WF = [WFTemperatureMax_w_nan[_DayPeriod], WFTemperatureMin_w_nan[_DayPeriod],WFTemperatureMean_w_nan[_DayPeriod], WFHumidityMax_w_nan[_DayPeriod], WFHumidityMin_w_nan[_DayPeriod], WFHumidityMean_w_nan[_DayPeriod]]
|
|
|
+ #MSE의 변화를 확인하기 위하여 앙상블의 크기 범위에서 랜덤 포레스트 트레이닝
|
|
|
+ maeOos = []
|
|
|
+ Acc_CVRMSE = []
|
|
|
+ Acc_MBE = []
|
|
|
+ nTreeList = range(100, 200, 50)
|
|
|
+ for iTrees in nTreeList:
|
|
|
+ depth = None
|
|
|
+ maxFeat = np.matrix(X).shape[1] #조정해볼 것
|
|
|
+ DailyDeicing_RFModel = ensemble.RandomForestRegressor(n_estimators=iTrees,
|
|
|
+ max_depth=depth, max_features=maxFeat,
|
|
|
+ oob_score=False, random_state=42)
|
|
|
+ DailyDeicing_RFModel.fit(xTrain, yTrain)
|
|
|
+ #데이터 세트에 대한 MSE 누적
|
|
|
+ prediction = DailyDeicing_RFModel.predict(xVal)
|
|
|
+
|
|
|
+ maeOos.append(MAE(yVal, prediction))
|
|
|
+ Acc_MBE.append(MBE(yVal, prediction))
|
|
|
+ Acc_CVRMSE.append(CVRMSE(np.array(yVal), np.array(prediction)))
|
|
|
+ #print('prediction', prediction)
|
|
|
+ #print('yVal', yVal)
|
|
|
+
|
|
|
+ #print("Validation Set of MAE : ",maeOos[-1])
|
|
|
+ #print("Validation Set of CVRMSE : ", CVRMSE(yVal, prediction))
|
|
|
+ #print("Validation Set of Aver. CVRMSE : ", np.mean(Acc_CVRMSE))
|
|
|
+
|
|
|
+ PredictedDeIcingAmount = DailyDeicing_RFModel.predict([xTomorrow_WF]) ## 학습모델을 통한 익일 방냉량 예측
|
|
|
+ PredictedDeIcingAmount_Tomorrow = round(PredictedDeIcingAmount[0],6)
|
|
|
+ print('####################################################')
|
|
|
+ print('## Estimated daily Deicing amount = ', PredictedDeIcingAmount_Tomorrow, ' % ##')
|
|
|
+ print('####################################################')
|
|
|
+
|
|
|
+ #### 익일 방냉량 DB 삽입
|
|
|
+ ### Day-ahead deicing amount is updated everyday
|
|
|
+ # MSSQL Access
|
|
|
+ conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
|
|
|
+ # Create Cursor from Connection
|
|
|
+ cursor = conn.cursor()
|
|
|
+
|
|
|
+ if now.hour >= 21:
|
|
|
+ TargetDate = datetime.datetime(now.year,now.month,now.day,21,0,0) + datetime.timedelta(days=1)
|
|
|
+ else:
|
|
|
+ TargetDate = datetime.datetime(now.year,now.month,now.day,21,0,0)
|
|
|
+
|
|
|
+ ## Storage deicing amount
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsMonitoringPointForecastingDayAhead where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 0 and TargetDateTime = '" + TargetDate.strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsMonitoringPointForecastingDayAhead set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', ForecastedValue = " + str(PredictedDeIcingAmount_Tomorrow) + " where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 0 and TargetDateTime = '"+ TargetDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The prediction of Daily deicing amount was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Daily deicing amount)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsMonitoringPointForecastingDayAhead (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,ForecastedValue) VALUES(1,3,4478,0,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TargetDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(PredictedDeIcingAmount_Tomorrow) + ")" )
|
|
|
+
|
|
|
+ print("* The prediction of daily deicing amount was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Daily deicing amount)")
|
|
|
+
|
|
|
+
|
|
|
+ print('************ (Finish) The Degree of Daily Deicing is being predicted!! ************')
|
|
|
+ print('***********************************************************************************')
|
|
|
+ #######################################################################################
|
|
|
+
|
|
|
+
|
|
|
+ ##################################################################################################################################################
|
|
|
+ ################# Find Optimal Operating Schedule for predicted daily deicing amount #############################################################
|
|
|
+ ## 15분 주기로 현상태 반영하여 업데이트
|
|
|
+
|
|
|
+ print('************ (Start) Recommended operating schedule is being found!! ************')
|
|
|
+
|
|
|
+ if now.hour >= 0 and now.hour < 21:
|
|
|
+ simul_lth = 24*4 - (now.hour*4 + int(now.minute/15)) - 3*4 ## (15분 단위 카운트)
|
|
|
+ else:
|
|
|
+ simul_lth = 24*4 - (now.hour*4 +int(now.minute/15)) + 21*4
|
|
|
+ # 이미 지난 시간(전날 9 pm 이후)에 대한 데이터 정리
|
|
|
+ inputX_prev = ChillerStatus_wo_nan[len(ChillerStatus_wo_nan)-(96-simul_lth):len(ChillerStatus_wo_nan)]
|
|
|
+ inputX_REF1_prev = RefStatus1_wo_nan[len(RefStatus1_wo_nan)-(96-simul_lth):len(RefStatus1_wo_nan)]
|
|
|
+ inputX_REF2_prev = RefStatus2_wo_nan[len(RefStatus2_wo_nan)-(96-simul_lth):len(RefStatus2_wo_nan)]
|
|
|
+ RecommendedCalAmount_prev = CalAmount_wo_nan[len(CalAmount_wo_nan)-(96-simul_lth):len(CalAmount_wo_nan)]
|
|
|
+
|
|
|
+ print('* Current Amount : ', CalAmount_wo_nan[-1], '[%], ', 'Estimated Deicing Amount : ', PredictedDeIcingAmount_Tomorrow, '[%]')
|
|
|
+ idx = 0
|
|
|
+ TermNum = 96
|
|
|
+ RecommendedCalAmount = [CalAmount_wo_nan[-1]]
|
|
|
+
|
|
|
+ if now.hour >= 21 or now.hour < 6:
|
|
|
+ while RecommendedCalAmount[-1] < PredictedDeIcingAmount_Tomorrow:
|
|
|
+ idx += 1
|
|
|
+ if idx >= simul_lth:
|
|
|
+ print("* It should be fully operated")
|
|
|
+ break
|
|
|
+ inputX = []
|
|
|
+ inputX_REF1 = []
|
|
|
+ inputX_REF2 = []
|
|
|
+ ## 단순히 심야 운전만 고려하고 축냉량 시 제빙모드와 OFF만 고려하여 시뮬레이션 (다른 모드를 추가하여 구성할 수 있음)
|
|
|
+ ## Off=0, Icing = 10, StorageOnly = 20, Parallel = 30, ChillerOnly = 40
|
|
|
+ ## 추천 방냉은 저녁 9시 이후부터 아침 6시 사이까지.... 중간에 사용하고 있는 부분에 대한 것은 어떻게 처리할지...고민해야함...낮에 축단운전을 하기에....
|
|
|
+ for i in range(idx):
|
|
|
+ inputX.append(Icing)
|
|
|
+ inputX_REF1.append(1)
|
|
|
+ inputX_REF2.append(1)
|
|
|
+ for i in range(simul_lth-len(inputX)):
|
|
|
+ inputX.append(0)
|
|
|
+ inputX_REF1.append(0)
|
|
|
+ inputX_REF2.append(0)
|
|
|
+
|
|
|
+ RecommendedCalAmount = [CalAmount_wo_nan[-1]]
|
|
|
+ for i in range(len(inputX)):
|
|
|
+ if i == 1:
|
|
|
+ RecommendedCalAmount = RecommendedCalAmount[-1]
|
|
|
+ if inputX[i]==Icing:
|
|
|
+ if inputX_REF1[i] + inputX_REF2[i]==2:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing))
|
|
|
+ elif inputX_REF1[i] + inputX_REF2[i]==1:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing)/2)
|
|
|
+ else:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1])
|
|
|
+
|
|
|
+ elif inputX[i]==StorageOnly:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_StorageOnly))
|
|
|
+
|
|
|
+ elif inputX[i]==Parallel:
|
|
|
+ if inputX_REF1[i] + inputX_REF2[i]==2:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel)*2)
|
|
|
+ elif inputX_REF1[i] + inputX_REF2[i]==1:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
|
|
|
+ else:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1])
|
|
|
+
|
|
|
+ elif inputX[i]==ChillerOnly:
|
|
|
+ if inputX_REF1[i] + inputX_REF2[i]==2:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
|
|
|
+ elif inputX_REF1[i] + inputX_REF2[i]==1:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly)/2)
|
|
|
+ else:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1])
|
|
|
+
|
|
|
+ elif inputX[i]==0:
|
|
|
+ RecommendedCalAmount.append(RecommendedCalAmount[-1])
|
|
|
+
|
|
|
+ ## 0이나 100을 넘어갔을 경우 보정 (현재 데이터에서 축열량은 % 단위이기 때문에)
|
|
|
+ if RecommendedCalAmount[-1] >= 100:
|
|
|
+ RecommendedCalAmount[-1] = 100
|
|
|
+ elif RecommendedCalAmount[-1] <= 0:
|
|
|
+ RecommendedCalAmount[-1] = 0
|
|
|
+ #print('max.',np.max(RecommendedCalAmount[-1]))
|
|
|
+
|
|
|
+ else:
|
|
|
+ print("************ It is not time to operate the storage in icing mode ")
|
|
|
+
|
|
|
+ if idx == 0:
|
|
|
+ inputX = []
|
|
|
+ inputX_REF1 = []
|
|
|
+ inputX_REF2 = []
|
|
|
+ RecommendedCalAmount = []
|
|
|
+ for i in range(simul_lth):
|
|
|
+ inputX.append(0)
|
|
|
+ inputX_REF1.append(0)
|
|
|
+ inputX_REF2.append(0)
|
|
|
+ RecommendedCalAmount.append(CalAmount_wo_nan[-1])
|
|
|
+ inputX = inputX_prev + inputX
|
|
|
+ inputX_REF1 = inputX_REF1_prev + inputX_REF1
|
|
|
+ inputX_REF2 = inputX_REF2_prev + inputX_REF2
|
|
|
+ RecommendedCalAmount = RecommendedCalAmount_prev + RecommendedCalAmount
|
|
|
+
|
|
|
+ #### 실제 및 추천 운전 스케쥴 DB 삽입
|
|
|
+ #### Recommended operating schedule is updated everyday
|
|
|
+ # MSSQL Access
|
|
|
+ conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
|
|
|
+ # Create Cursor from Connection
|
|
|
+ cursor = conn.cursor()
|
|
|
+
|
|
|
+ # Execute SQL
|
|
|
+ if now.hour >= 21:
|
|
|
+ InitDate = datetime.datetime(now.year,now.month,now.day,21,0,0)
|
|
|
+ else:
|
|
|
+ InitDate = datetime.datetime(now.year,now.month,now.day,21,0,0)-datetime.timedelta(days=1)
|
|
|
+
|
|
|
+ ## Storage mode
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 16 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(inputX[i]) + " where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 16 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The storage operating schedule was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Ice storage mode)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,3,4478,16,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(inputX[i]) + ", 0)" )
|
|
|
+
|
|
|
+ print("* The storage operating schedule was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Ice storage mode)")
|
|
|
+
|
|
|
+ ## REF1 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(inputX_REF1[i]) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The refrigerator1 status was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[Error] There is an update error!! (Recommended refrigerator1 status)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4479,15,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(inputX_REF1[i]) + ", 0)" )
|
|
|
+
|
|
|
+ print("* The refrigerator1 status was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[Error] There is an insert error!! (Recommended refrigerator1 status)")
|
|
|
+
|
|
|
+ ## REF1 power consume
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ if inputX_REF1[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume1
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(TmpComsume) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The recommended refrigerator1 power was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Recommended refrigerator1 power)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ if inputX_REF1[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume1
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4479,11,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(TmpComsume) + ", 0)" )
|
|
|
+
|
|
|
+ print("* The recommended refrigerator1 power was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Recommended refrigerator1 power)")
|
|
|
+
|
|
|
+ ## REF2 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(inputX_REF2[i]) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The refrigerator2 status was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Recommended refrigerator2 status)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4480,15,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(inputX_REF2[i]) + ", 0)" )
|
|
|
+
|
|
|
+ print("* The refrigerator2 status was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Recommended refrigerator2 status)")
|
|
|
+
|
|
|
+ ## REF2 power consume
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ if inputX_REF2[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume2
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(TmpComsume) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* The recommended refrigerator2 power was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Recommended Refrigerator2 power)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ if inputX_REF2[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume2
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4480,11,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(TmpComsume) + ", 0)" )
|
|
|
+
|
|
|
+ print("* The refrigerator2 power was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Recommended Refrigerator2 power)")
|
|
|
+
|
|
|
+ ## Thermal energy amount
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 0 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:00:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData=[]
|
|
|
+ while row:
|
|
|
+ row = cursor.fetchone()
|
|
|
+ rawData.append(row)
|
|
|
+ if rawData:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(RecommendedCalAmount[i]) + " where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ print("* Thermal energy amount was updated!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Recommended thermal energy amount)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ for i in range(TermNum):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,3,4478,2,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(RecommendedCalAmount[i]) + ", 0)" )
|
|
|
+
|
|
|
+ print("* Thermal energy amount was inserted!! (Recommend)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Recommended thermal energy amount)")
|
|
|
+
|
|
|
+ ## 첫 실행시에만 동작
|
|
|
+ if Init:
|
|
|
+ ## Thermal energy amount (과거 확인 후 축열량이 공백인 경우 채워주기)
|
|
|
+ CalAmount_prev_tmp = CalAmount_prev[len(CalAmount_prev)-TermNum*5:]
|
|
|
+ for d in range(5, 0, -1): # 5일전까지
|
|
|
+ InitDate_tmp = InitDate-datetime.timedelta(days=d)
|
|
|
+
|
|
|
+ for m in range(TermNum): # 1열씩 업데이트 (중간중간 공백인 경우를 고려)
|
|
|
+ TmpDate = InitDate_tmp + datetime.timedelta(minutes=m*15)
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 0 and TargetDateTime = '" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "' order by CreatedDateTime desc")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row:
|
|
|
+ try:
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ (datetime.datetime.now()-datetime.timedelta(minutes=d)).strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(CalAmount_prev_tmp[(5-d)*TermNum+m]) + " where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 0 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Recommended thermal energy amount)")
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,3,4478,2,'" + (datetime.datetime.now()-datetime.timedelta(minutes=d)).strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(CalAmount_prev_tmp[(5-d)*TermNum+m]) + ", 0)" )
|
|
|
+
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Recommended thermal energy amount)")
|
|
|
+
|
|
|
+ conn.close()
|
|
|
+
|
|
|
+ print('************ (Finish) Recommended operating schedule is being found!! ************')
|
|
|
+ print('**********************************************************************************')
|
|
|
+ #######################################################################################
|
|
|
+
|
|
|
+
|
|
|
+ ##################################################################################################################################################
|
|
|
+ ################# Stochastic method for estimating the Variation of Ice Thermal Storage based on Operation Mode "for Simulation" #################
|
|
|
+ #### 사용자 정의 데이터를 데이터 로드
|
|
|
+ ### 계속 체킹
|
|
|
+
|
|
|
+ while True:
|
|
|
+ now_ = datetime.datetime.now().now()
|
|
|
+ ## sleep 매분 2,6,10,... 초에만 동작
|
|
|
+ if now_.second%4==2:
|
|
|
+ break
|
|
|
+ time.sleep(1)
|
|
|
+
|
|
|
+ #time.sleep(2)
|
|
|
+ #print('start time : ', now_)
|
|
|
+ # MSSQL Access
|
|
|
+ conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
|
|
|
+ # Create Cursor from Connection
|
|
|
+ cursor = conn.cursor()
|
|
|
+
|
|
|
+ # Execute SQL
|
|
|
+ cursor.execute('SELECT TOP 1 * FROM '+targetDBName+'.dbo.BemsIceThermalStorageSimulation where SiteId=1 and FacilityCode=4478 and PropertyId=16 and SimulationCase=1 order by CreatedDateTime desc')
|
|
|
+
|
|
|
+
|
|
|
+ row = cursor.fetchone()
|
|
|
+ conn.close()
|
|
|
+ #print('end time : ', now_)
|
|
|
+ if Init:
|
|
|
+ if row != None:
|
|
|
+ recentDateTime = row[4]
|
|
|
+ else:
|
|
|
+ recentDateTime = now_
|
|
|
+ Init = False
|
|
|
+ ActiveSimulator = False
|
|
|
+ if row != None:
|
|
|
+ if recentDateTime < row[4]:
|
|
|
+ recentDateTime = row[4]
|
|
|
+ ActiveSimulator = True
|
|
|
+ else:
|
|
|
+ ActiveSimulator = False
|
|
|
+
|
|
|
+ now_ = datetime.datetime.now().now()
|
|
|
+ if now_.second%30 > 0 and now_.second%30 < 2:
|
|
|
+ print('* Keep an eye on updating DB table every 2 seconds ... (This message appears every 30 seconds)')
|
|
|
+
|
|
|
+ if ActiveSimulator:
|
|
|
+ print('************ (Start) Simulator! ************')
|
|
|
+ time.sleep(2)
|
|
|
+ # MSSQL Access
|
|
|
+ conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
|
|
|
+ # Create Cursor from Connection
|
|
|
+ cursor = conn.cursor()
|
|
|
+ # Execute SQL
|
|
|
+ InitDate = datetime.datetime(now.year, now.month, now.day, now.hour, int(int(now.minute/15)*15),0)
|
|
|
+
|
|
|
+ ## Storage mode
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 16 and SimulationCase = 1 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:%M:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by TargetDateTime asc")
|
|
|
+ # 데이타 한꺼번에 Fetch
|
|
|
+ rows = cursor.fetchall()
|
|
|
+ rawData_StorageMode = []
|
|
|
+ for i in rows:
|
|
|
+ rawData_StorageMode.append(i)
|
|
|
+
|
|
|
+ time.sleep(1)
|
|
|
+ ## REF1 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 and SimulationCase = 1 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:%M:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by TargetDateTime asc")
|
|
|
+ # 데이타 한꺼번에 Fetch
|
|
|
+ rows = cursor.fetchall()
|
|
|
+ rawData_RefStatus1 = []
|
|
|
+ for i in rows:
|
|
|
+ rawData_RefStatus1.append(i)
|
|
|
+
|
|
|
+ # rawData_RefStatus1=rawData_RefStatus1[:len(rawData_RefStatus1)-1]
|
|
|
+ #rawData_RefStatus1=rawData_RefStatus1[:-2]
|
|
|
+
|
|
|
+ time.sleep(1)
|
|
|
+ ## REF2 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 and SimulationCase = 1 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:%M:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by TargetDateTime asc")
|
|
|
+ # 데이타 한꺼번에 Fetch
|
|
|
+ rows = cursor.fetchall()
|
|
|
+ rawData_RefStatus2 = []
|
|
|
+ for i in rows:
|
|
|
+ rawData_RefStatus2.append(i)
|
|
|
+ # rawData_RefStatus2=rawData_RefStatus2[:len(rawData_RefStatus2)-1]
|
|
|
+ # rawData_RefStatus2=rawData_RefStatus2[:-2]
|
|
|
+
|
|
|
+ CustomizedStatus=[]
|
|
|
+ for i in range(len(rawData_StorageMode)):
|
|
|
+ CustomizedStatus.append(rawData_StorageMode[i][6])
|
|
|
+
|
|
|
+ CustomizedRefStatus1=[]
|
|
|
+ for i in range(len(rawData_RefStatus1)):
|
|
|
+ CustomizedRefStatus1.append(rawData_RefStatus1[i][6])
|
|
|
+
|
|
|
+ CustomizedRefStatus2 = []
|
|
|
+ for i in range(len(rawData_RefStatus2)):
|
|
|
+ CustomizedRefStatus2.append(rawData_RefStatus2[i][6])
|
|
|
+
|
|
|
+ # 한번 더 데이터 불러오기 (가끔 제대로 로드 안되는 경우 있음)
|
|
|
+ time.sleep(0.5)
|
|
|
+ if len(CustomizedStatus) != len(CustomizedRefStatus1):
|
|
|
+ ## REF1 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 and SimulationCase = 1 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:%M:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by TargetDateTime asc")
|
|
|
+ # 데이타 한꺼번에 Fetch
|
|
|
+ rows = cursor.fetchall()
|
|
|
+ rawData_RefStatus1 = []
|
|
|
+ for i in rows:
|
|
|
+ rawData_RefStatus1.append(i)
|
|
|
+
|
|
|
+ CustomizedRefStatus1=[]
|
|
|
+ for i in range(len(rawData_RefStatus1)):
|
|
|
+ CustomizedRefStatus1.append(rawData_RefStatus1[i][6])
|
|
|
+
|
|
|
+ time.sleep(0.5)
|
|
|
+ if len(CustomizedStatus) != len(CustomizedRefStatus2):
|
|
|
+ ## REF2 status
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 and SimulationCase = 1 and TargetDateTime >= '" + InitDate.strftime('%Y-%m-%d %H:%M:00') + "' and TargetDateTime < '" + (InitDate + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:00:00') + "' order by TargetDateTime asc")
|
|
|
+ # 데이타 한꺼번에 Fetch
|
|
|
+ rows = cursor.fetchall()
|
|
|
+ rawData_RefStatus2 = []
|
|
|
+ for i in rows:
|
|
|
+ rawData_RefStatus2.append(i)
|
|
|
+
|
|
|
+ CustomizedRefStatus2 = []
|
|
|
+ for i in range(len(rawData_RefStatus2)):
|
|
|
+ CustomizedRefStatus2.append(rawData_RefStatus2[i][6])
|
|
|
+
|
|
|
+
|
|
|
+ SimulCalAmount=[CalAmount_wo_nan[-1]]
|
|
|
+ for i in range(len(CustomizedStatus)):
|
|
|
+ if i == 1:
|
|
|
+ SimulCalAmount = [SimulCalAmount[-1]]
|
|
|
+ ## 제빙운전은 두대로 운영되었으므로 평균값은 2대 운전 기준
|
|
|
+ if CustomizedStatus[i] == Icing:
|
|
|
+ if len(GradientCalAmount_w3sigma_mode_Icing) == 0:
|
|
|
+ print('[Warning] There is no enough data (Icing)')
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ else:
|
|
|
+ if CustomizedRefStatus1[i] + CustomizedRefStatus2[i] == 2:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing))
|
|
|
+ elif CustomizedRefStatus1[i] + CustomizedRefStatus2[i] == 1:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing)/2)
|
|
|
+ else:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ ## 축단운전은 냉동기가 운영되지 않음
|
|
|
+ elif CustomizedStatus[i] == StorageOnly:
|
|
|
+ if len(GradientCalAmount_w3sigma_mode_StorageOnly) == 0:
|
|
|
+ print('[Warning] There is no enough data (Storage Only)')
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ else:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_StorageOnly))
|
|
|
+ ## 병렬운전에서 축열조 변화량은 냉동기 상태와 상관없음
|
|
|
+ elif CustomizedStatus[i] == Parallel:
|
|
|
+ if len(GradientCalAmount_w3sigma_mode_Parallel) == 0:
|
|
|
+ print('[Warning] There is no enough data (Parallel)')
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ else:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
|
|
|
+ ## 냉단운전은 냉동기 두대로 운영되었으므로 축열량은 그대로
|
|
|
+ elif CustomizedStatus[i] == ChillerOnly:
|
|
|
+ if len(GradientCalAmount_w3sigma_mode_ChillerOnly) == 0:
|
|
|
+ print('[Warning] There is no enough data (Chiller Only)')
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ else:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+ elif CustomizedStatus[i]==0:
|
|
|
+ SimulCalAmount.append(SimulCalAmount[-1])
|
|
|
+
|
|
|
+ if SimulCalAmount[-1] > 100:
|
|
|
+ SimulCalAmount[-1] = 100
|
|
|
+ CustomizedRefStatus1[i] = 0
|
|
|
+ CustomizedRefStatus2[i] = 0
|
|
|
+ elif SimulCalAmount[-1] < 0:
|
|
|
+ SimulCalAmount[-1] = 0
|
|
|
+ CustomizedRefStatus1[i] = 0
|
|
|
+ CustomizedRefStatus2[i] = 0
|
|
|
+
|
|
|
+
|
|
|
+ #### 시뮬레이션 결과 데이터 DB 삽입
|
|
|
+ ## Thermal energy amount
|
|
|
+ for i in range(len(CustomizedStatus)):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 1 and TargetDateTime = '" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "' ")
|
|
|
+
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row:
|
|
|
+ try:
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(SimulCalAmount[i]) + " where SiteId = 1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 and SimulationCase = 1 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* Thermal energy amount was updated!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Simulated thermal energy amount)")
|
|
|
+
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,3,4478,2,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(SimulCalAmount[i]) + ", 1)" )
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* Thermal energy amount was inserted!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Simulated thermal energy amount)")
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+ ## REF1 power consume
|
|
|
+ for i in range(len(CustomizedStatus)):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 and SimulationCase = 1 and TargetDateTime = '" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "' ")
|
|
|
+
|
|
|
+ if CustomizedRefStatus1[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume1
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row:
|
|
|
+ try:
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(TmpComsume) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 and SimulationCase = 1 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* The REF1 power comsumption was updated!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Simulated refrigerator1 power)")
|
|
|
+
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4479,11,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(TmpComsume) + ", 1)" )
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* The REF1 power comsumption was inserted!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Simulated refrigerator1 power)")
|
|
|
+
|
|
|
+ ## REF2 power consume
|
|
|
+
|
|
|
+ for i in range(len(CustomizedStatus)):
|
|
|
+ TmpDate = InitDate + datetime.timedelta(minutes=i*15)
|
|
|
+ cursor.execute("SELECT * FROM " + targetDBName + ".dbo.BemsIceThermalStorageSimulation where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 and SimulationCase = 1 and TargetDateTime = '" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "' ")
|
|
|
+
|
|
|
+ if CustomizedRefStatus2[i]==1:
|
|
|
+ TmpComsume = mean_RefConsume2
|
|
|
+ else:
|
|
|
+ TmpComsume = 0
|
|
|
+ # 데이타 하나씩 Fetch하여 출력
|
|
|
+ row = cursor.fetchone()
|
|
|
+ if row:
|
|
|
+ try:
|
|
|
+ cursor.execute("UPDATE " + targetDBName + ".dbo.BemsIceThermalStorageSimulation set CreatedDateTime = '"+ datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +"', SimulationValue = " + str(TmpComsume) + " where SiteId = 1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 and SimulationCase = 1 and TargetDateTime = '"+ TmpDate.strftime('%Y-%m-%d %H:%M:00')+"'")
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* The REF2 power comsumption was updated!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an update error!! (Simulated refrigerator2 power)")
|
|
|
+
|
|
|
+ else:
|
|
|
+ try:
|
|
|
+ cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsIceThermalStorageSimulation (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,SimulationValue,SimulationCase) VALUES(1,2,4480,11,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + TmpDate.strftime('%Y-%m-%d %H:%M:00') + "', "+ str(TmpComsume) + ", 1)" )
|
|
|
+
|
|
|
+ if i == len(CustomizedStatus)-1:
|
|
|
+ print("* The REF2 power comsumption was inserted!! (Simul)")
|
|
|
+ except:
|
|
|
+ print("[ERROR] There is an insert error!! (Simulated refrigerator2 power)")
|
|
|
+
|
|
|
+ conn.close()
|
|
|
+ print('************ (Finish) Simulator! ************')
|
|
|
+ print('*********************************************')
|
|
|
+ #######################################################################################
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|