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- # # Day-ahead load forecasting
- #
- # DB : MS SQL
- #
- # Program Language : Python
- #
- # kgpark@hdc-icontrols.com
- # April 10, 2020
- # ### BEMS 데이터 수집 메카니즘
- # #### 데이터 별로 수집 타입에 따라 다르지만, Raw 테이블에 적산 값으로 저장이 되고 15min 테이블에서 해당 시간대와 그 전 시간대의 차이 값을 입력한다.
- # #### DGW 혹은 시스템에 이상이 생겼을 때, 데이터가 들어오지 않거나 0으로 입력된다.
- # #### 1시간 테이블은 15분 테이블에서 각 15분, 30분, 45분, 60분의 데이터 합산 값이 나왔다.
- # #### 합산 값으로 저장되다보니 4개 포인트 중 적어도 하나만 있어도 1시간 데이터로 저장이 된다.
- # #### 따라서, 15분 데이터를 전처리하는 것이 주효하고 데이터가 없거나 0값을 검출하여 비정상 데이터로 추정하는 것을 추천한다.
- # #### 또한, 1시간 단위로 데이터 주기를 변환한다면 15분 테이블의 4개 포인트 중 하나라도 값을 모른다면 그 시간의 데이터가 비정상이라고 가정하는 것을 추천한다.
- import matplotlib.pyplot as plt
- import pymssql
- import datetime
- import numpy as np
- import math
- from korean_lunar_calendar import KoreanLunarCalendar
- import calendar
- import configparser
- import sys
- import time
- # ## Define functions
- ### Define day-type
- def getDayName(year, month, day):
- return ['MON','TUE','WED','THU','FRI','SAT','SUN'][datetime.date(year, month, day).weekday()]
- def getDayType(DateinDay, Period, SpecialHoliday):
- DoW=[]; # Day of Week
- for i in range(Period):
- if DateinDay[i].year==2019 and DateinDay[i].month==5 and DateinDay[i].day==18:
- DoW.append([5, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'MON':
- DoW.append([1, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'TUE':
- DoW.append([2, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'WED':
- DoW.append([3, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'THU':
- DoW.append([4, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'FRI':
- DoW.append([5, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SAT':
- DoW.append([6, DateinDay[i]])
- elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SUN':
- DoW.append([7, DateinDay[i]])
- for j in range(len(SpecialHoliday)):
- if SpecialHoliday[j] == datetime.date(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day):
- DoW[-1][0] = 8
- break
- ### W:1, N:2, ### W: Workday, N: Non-workday
- DayType=[]
- for i in range(Period):
- if DoW[i][0] <= 5:
- DayType.append([1, DateinDay[i]])
- elif DoW[i][0] > 5:
- DayType.append([2, DateinDay[i]])
- return DoW, DayType
- def Reconstruction(DayType, DatainHour, mark, DataRes, isRecent):
- ReconstructedData=[]
- DayType1h=[]
- Day_len = len(DayType)
- # Rearrange data in hour unit
- for i in range(Day_len):
- if i == Day_len-1 and isRecent:
- Time_len = len(DatainHour) - i*DataRes
- else:
- Time_len=DataRes
- for j in range(Time_len):
- DayType1h.append([DatainHour[i*DataRes + j], DayType[i][0], datetime.datetime(DayType[i][1].year, DayType[i][1].month, DayType[i][1].day, j, 0)]) ## data, daytype, time
-
- # 비정상 데이터보다 앞선 시간의 데이터 중 DayType이 같고 시간이 같은 5개 날 데이터의 평균으로 복원함
- for i in reversed(range(len(DayType1h))):
- AccData=[]
- cnt=0
- if math.isnan(DayType1h[i][0]):
- for j in range(len(DayType1h)):
- if cnt > 5:
- break
- if i < j and DayType1h[j][1] == DayType1h[i][1] and DayType1h[j][2].hour == DayType1h[i][2].hour and (not math.isnan(DayType1h[j][0])):
- AccData.append(DayType1h[j][0])
- cnt += 1
- DayType1h[i][0] = np.mean(AccData)
- ReconstructedData.append(DayType1h[i][0])
- ReconstructedData.reverse()
- ### Double-checking for the data which is not reconstructed, especially in front
- for i in range(len(DayType1h)):
- AccData=[]
- cnt=0
- if math.isnan(DayType1h[i][0]):
- #print('Here is NaN!!',ReconstructedData[i],i,DayType1h[i][2].hour, DayType1h[i][1])
- for j in reversed(range(len(DayType1h))):
- if cnt > 5:
- break
- if i > j and DayType1h[j][1] == DayType1h[i][1] and DayType1h[j][2].hour == DayType1h[i][2].hour and (not math.isnan(DayType1h[j][0])):
- AccData.append(DayType1h[j][0])
- cnt += 1
- ReconstructedData[i] = np.mean(AccData)
- return ReconstructedData, DayType1h
- ## For day-ahead linear prediction
- def lpc_pred_DayAhead(Data_trn, DayType_trn, cov_lth, DayType_tst, DataRes):
- # Calculating the filter bank for each hour and day-type using traing set
- for c_w in range(1,3):
- DayType_trn[0,0]=0
- CP_pred_fb=np.zeros(Data_trn.shape)
- lpc_fb=np.zeros([cov_lth[c_w-1],DataRes])
- Prv_A=[]
- Prv_A=np.transpose(Data_trn[DataRes-cov_lth[c_w-1]:DataRes,np.where(DayType_trn == c_w)[0]-1])
- for hr_i in range(24):
- lpc_fb[:,hr_i]=np.dot(np.linalg.pinv(Prv_A), np.transpose(Data_trn[hr_i,np.where(DayType_trn == c_w)[0]]))
- if c_w == 1:
- lpc_fb1=lpc_fb
- elif c_w == 2:
- lpc_fb2=lpc_fb
- ## For testing
- if DayType_tst[0,0] == 1:
- lpc_t=lpc_fb1
- elif DayType_tst[0,0] == 2:
- lpc_t=lpc_fb2
- Data_tt=Data_trn[:,-1]
- # Load prediction for test day based on the filter bank
- CP_pred=np.transpose(np.dot(np.transpose(Data_tt[DataRes-cov_lth[DayType_tst[0,0]-1]:DataRes+1]),lpc_t))
- return CP_pred
- ## For step-ahead linear prediction
- def lpc_pred_OneStepAhead(Data_trn, DayType_trn, cov_lth, DayType_tst, DataRes):
- for c_w in range(1,3):
- DayType_trn[0,0]=0
- lpc_fb=np.zeros([cov_lth[c_w-1],DataRes])
- Prv_A=[]
- Prv_A=np.transpose(Data_trn[DataRes-cov_lth[c_w-1]:DataRes,np.where(DayType_trn == c_w)[0]-1])
- lpc_fb=np.dot(np.linalg.pinv(Prv_A), np.transpose(Data_trn[0,np.where(DayType_trn == c_w)[0]]))
- if c_w == 1:
- lpc_fb1=lpc_fb
- elif c_w == 2:
- lpc_fb2=lpc_fb
- ## Testing
- if DayType_tst[0,0] == 1:
- lpc_t=lpc_fb1
- elif DayType_tst[0,0] == 2:
- lpc_t=lpc_fb2
-
- Data_tt=Data_trn[:,-1]
- CP_pred=np.transpose(np.dot(np.transpose(Data_tt[DataRes-cov_lth[DayType_tst[0,0]-1]:DataRes+1]),lpc_t))
- return CP_pred
- ## Measure
- def MAPE(y_observed, y_pred):
- return np.mean(np.abs((y_observed - y_pred) / y_observed)) * 100
- def MAE(y_observed, y_pred):
- return np.mean(np.abs(y_observed - y_pred))
- def MBE(y_observed, y_pred):
- return (np.sum((y_observed - y_pred))/(len(y_observed)*np.mean(y_observed)))*100
- def CVRMSE(y_observed, y_pred):
- return (np.sqrt(np.mean((y_observed - y_pred)*(y_observed - y_pred)))/np.mean(y_observed))*100
- ## Check for normal time stamp
- def Check_AlivedTimeStamp(RawData, ComparedData, idx_raw, idx_comp):
- if datetime.date(RawData[idx_raw][4].year,RawData[idx_raw][4].month,RawData[idx_raw][4].day) == datetime.date(ComparedData[idx_comp].year, ComparedData[idx_comp].month, ComparedData[idx_comp].day) and datetime.time(RawData[idx_raw][4].hour,RawData[idx_raw][4].minute) == datetime.time(ComparedData[idx_comp].hour, ComparedData[idx_comp].minute):
- isAlived = True
- else:
- isAlived = False
- return isAlived
- if __name__ == "__main__" :
- ## Check every hour on the hour operating infinite loop
- while True:
- now = datetime.datetime.now().now()
-
- ## distinguish real time update and specific day
- ## 자정에 생기는 인덱싱 문제로 0시에는 16분에 업데이트
- if (now.hour != 0 and now.minute == 1) or (now.hour == 0 and now.minute == 16):
- PredctionActive = True
- else:
- PredctionActive = False
- if now.second > 55:
- print("[ Current Time -", now.hour,":", now.minute,":", now.second,"], " "Sleeping for 30 seconds... Prediction starts every hour")
- time.sleep(30)
- else:
- print("[ Current Time -", now.hour,":", now.minute,":", now.second,"], " "Sleeping for 60 seconds... Prediction starts every hour")
- time.sleep(60)
-
- if PredctionActive:
-
- ## Loading .ini file
- myINI = configparser.ConfigParser()
- myINI.read("Config.ini", "utf-8" )
- # MSSQL Access
- 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'))
- # Create Cursor from Connection
- cursor = conn.cursor()
- # Execute SQL (Electric consumption)
- cursor.execute('SELECT * FROM BemsConfigData where SiteId = 1')
- rowDB_info = cursor.fetchone()
-
- conn.close()
-
- loadDBIP = rowDB_info[1]
- loadDBUserID = rowDB_info[2]
- loadDBUserPW = rowDB_info[3]
- loadDBName = rowDB_info[4]
- targetDBIP = rowDB_info[5]
- targetDBUserID = rowDB_info[6]
- targetDBUserPW = rowDB_info[7]
- targetDBName = rowDB_info[8]
- linearFilterLength = rowDB_info[10]
-
- print("=================== Prediction start! ===================")
-
- startday = datetime.date(int(rowDB_info[9].year), int(rowDB_info[9].month), int(rowDB_info[9].day))
-
- # ## Data accumulation
- isRecent = True
- lastday = datetime.date(now.year, now.month, now.day)
- if startday < datetime.date(2017,1,1):
- print('[ERROR] 데이터 최소 시작 시점은 2017.01.01 입니다')
- elif startday > lastday:
- print('[ERROR] 예측 타깃 시작시점이 데이터 시작 시점보다 작을 수 없습니다')
-
- now_ = datetime.date(now.year, now.month, now.day)
- # 학습데이터의 기간은 최대 2년으로 한정
- if (startday-now_).days > 730:
- Ago_2year = now_ + timedelta(days=-730)
- startday = datetime.date(Ago_2year.year, Ago_2year.month, Ago_2year.day)
-
- # MSSQL Access
- conn = pymssql.connect(host = loadDBIP, user = loadDBUserID, password = loadDBUserPW, database = loadDBName)
- # Create Cursor from Connection
- cursor = conn.cursor()
- # Execute SQL (Electric consumption)
- cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId = 1 and FacilityTypeId = 99 and FacilityCode = 4863 and PropertyId = 1 order by CreatedDateTime desc')
- # 데이타 하나씩 Fetch하여 출력
- row = cursor.fetchone()
- DataRes_org=96
- DataRes_24=24
- rawData=[]
- while row:
- row = cursor.fetchone()
- if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
- break
- rawData.append(row)
- rawData.reverse() # 오름차순 정렬
-
- # 연결 끊기
- conn.close()
- print('rawData',rawData[0],rawData[-1])
- # 현장 데이터가 없을 경우 예외처리
- if now.hour == 0:
- hour_calib = 0
- else:
- hour_calib = 1
- if datetime.datetime(now.year, now.month, now.day, now.hour, 0, 0) - datetime.timedelta(hours=hour_calib) == datetime.datetime(rawData[-1][4].year, rawData[-1][4].month, rawData[-1][4].day, rawData[-1][4].hour, 0, 0):
-
- # MSSQL Access
- conn = pymssql.connect(host = loadDBIP, user = loadDBUserID, password = loadDBUserPW, database = loadDBName)
- # Create Cursor from Connection
- cursor = conn.cursor()
- # 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()
- 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()
- suddenHolidayData.append(row)
- suddenHolidayData = suddenHolidayData[0:-1]
-
- # 연결 끊기
- conn.close()
-
- # 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))
- DayPeriod = (lastday - startday).days + 1
- print('First day:',startday,',', 'Last Day:', lastday,',','Current Time:', now)
- print('Day period :', DayPeriod)
-
- # ## Find unkown/zero data (Bad data)
- StartTime = datetime.datetime(int(startday.strftime('%Y')), int(startday.strftime('%m')), int(startday.strftime('%d')), 0, 0, 0)
- TimeStamp_DayUnit = []
- StandardTimeStamp = []
- # Create normal time stamp
- for idx_day in range(DayPeriod):
- TimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
- if isRecent and idx_day == DayPeriod-1:
- if now.hour == 0: # 예외처리용 (자정에 Day count가 안되는 현상)
- tmp_len = 1
- else:
- tmp_len = now.hour*4 + int(now.minute/15)
- for idx_time in range(tmp_len):
- StandardTimeStamp.append(StartTime)
- StartTime += datetime.timedelta(minutes = 15)
- else:
- for idx_time in range(DataRes_org):
- StandardTimeStamp.append(StartTime)
- StartTime += datetime.timedelta(minutes = 15)
- RawDate=[] # raw data (date)
- RawElectricLoad=[] # raw data (electric load)
- for i in range(len(rawData)):
- if datetime.date(rawData[i][4].year,rawData[i][4].month,rawData[i][4].day) >= startday:
- if datetime.date(rawData[i][4].year,rawData[i][4].month,rawData[i][4].day) <= lastday:
- RawDate.append(rawData[i][4])
- RawElectricLoad.append(rawData[i][5])
- if datetime.date(rawData[i][4].year,rawData[i][4].month,rawData[i][4].day) > lastday:
- break
- Data_len=len(RawDate)
- if isRecent:
- DataAct_len = (DayPeriod-1)*DataRes_org + now.hour*4 + int(now.minute/15)
- else:
- DataAct_len = DayPeriod*DataRes_org
- ### Unknown/zero data counts
- DataCount=[]
- for i in range(len(TimeStamp_DayUnit)):
- cnt_unk=0 # For Unknown data count
- cnt_zero=0 # zero data count
- for j in range(Data_len):
- if TimeStamp_DayUnit[i] == datetime.date(RawDate[j].year,RawDate[j].month,RawDate[j].day):
- cnt_unk += 1
- if RawElectricLoad[j] == 0:
- cnt_zero += 1
- if isRecent and i==len(TimeStamp_DayUnit)-1:
- DataCount.append([TimeStamp_DayUnit[i], now.hour*4 + int(now.minute/15) - cnt_unk, cnt_zero])
- else:
- DataCount.append([TimeStamp_DayUnit[i], DataRes_org-cnt_unk, cnt_zero])
-
- ## Visualization
- ## 월 인덱스 설정 ##
- idxCal=[]
- idxCalName=[]
- idxCal.append(0)
- for y_idx in range(lastday.year - startday.year + 1):
- if startday.year == lastday.year:
- for m_idx in range(lastday.month - startday.month + 1):
- month = startday.month + m_idx
- idxCal.append(idxCal[-1] + calendar.monthrange(startday.year, month)[1])
- idxCalName.append(calendar.month_name[month])
- else:
- if y_idx == 0: ## 첫번째 해
- for m_idx in range(13-startday.month):
- month = startday.month + m_idx
- idxCal.append(idxCal[-1] + calendar.monthrange(startday.year, month)[1])
- idxCalName.append(calendar.month_name[month])
- elif y_idx !=0 and y_idx == lastday.year - startday.year: ## 마지막 해
- for m_idx in range(lastday.month):
- month = m_idx + 1
- idxCal.append(idxCal[-1] + calendar.monthrange(lastday.year, month)[1])
- idxCalName.append(calendar.month_name[month])
- else:
- for m_idx in range(12):
- month = m_idx + 1
- idxCal.append(idxCal[-1] + calendar.monthrange(startday.year+y_idx, month)[1])
- idxCalName.append(calendar.month_name[month])
- DataCountMat=np.matrix(DataCount)
-
- print("The number of unknown data:",sum(DataCountMat[:,1]), ", The number of zero data:", sum(DataCountMat[:,2]))
-
- plt.figure(figsize=(16,9))
- plt.subplot(311)
- plt.plot(DataCountMat[:,1],label='Unknown data', linewidth = 2)
- plt.plot(DataCountMat[:,2],label='Zero data', linewidth = 2)
- # plt.xlabel('Months', fontsize = 16)
- plt.ylabel('Data counts', fontsize = 14)
- plt.legend(loc='upper left', fontsize = 14)
- plt.title("Unknown/zero electric load data per 15min. unit ("+str(startday.year)+"."+str(startday.month)+"."+str(startday.day)+" - "+str(lastday.year)+"."+str(lastday.month)+"."+str(lastday.day)+")", fontsize = 14)
- plt.xlim(idxCal[0], idxCal[-1])
- plt.xticks(idxCal, idxCalName, fontsize=6.5)
- plt.yticks(fontsize=14)
-
- print("Bad data detection complete!")
- ### NaN-padding after finding unknown data
- ######## 현재 DB 특성상 값이 0으로 찍히거나 시간테이블의 행 자체가 없는 경우가 있고, 이 데이터 1 step 앞뒤로 데이터가 비정상일 확률이 높으므로 비정상데이터 뿐만 아니라 앞뒤 1 step까지 nan으로 처리함
- ElectricLoad_Un_ZP=[]
- RawDate=[]
- idx=0
- idx2=0
- isBadData = False
-
- for i in range(DataAct_len):
- if datetime.date(rawData[idx][4].year,rawData[idx][4].month,rawData[idx][4].day) >= startday and datetime.date(rawData[idx][4].year,rawData[idx][4].month,rawData[idx][4].day) <= lastday:
- RawDate.append(StandardTimeStamp[idx2])
- if isBadData == True:
- ElectricLoad_Un_ZP.append(np.nan)
- isBadData=False
- elif rawData[idx][5]==0:
- ElectricLoad_Un_ZP[-1]=np.nan
- ElectricLoad_Un_ZP.append(np.nan)
- if rawData[idx+1][5] > 0 and Check_AlivedTimeStamp(rawData, StandardTimeStamp, idx+1, idx2+1):
- isBadData = True
- elif Check_AlivedTimeStamp(rawData, StandardTimeStamp, idx, idx2):
- ElectricLoad_Un_ZP.append(rawData[idx][5])
- else:
- ElectricLoad_Un_ZP[-1]=np.nan
- ElectricLoad_Un_ZP.append(np.nan)
- if rawData[idx+1][5] > 0 and Check_AlivedTimeStamp(rawData, StandardTimeStamp, idx+1, idx2+1):
- isBadData = True
- idx -= 1
- idx2 += 1
- idx += 1
-
- print('NaN-padding complete!')
- # ## Decimation to 1-hour period
- ElectricLoad_1h = []
- for i in range(DayPeriod):
- if i == DayPeriod-1 and isRecent:
- Time_len = DataAct_len - i*DataRes_org + 1
- else:
- Time_len = DataRes_org
- isNaN=False
- for j in range(Time_len):
- if ElectricLoad_Un_ZP[i*4 + j] == np.nan:
- isNaN=True
- if j%4==3:
- if isNaN:
- ElectricLoad_1h.append(np.nan)
- else:
- ElectricLoad_1h.append(sum(ElectricLoad_Un_ZP[i*DataRes_org + j-3:i*DataRes_org + j+1]))
- print('Decimation to 1hour complete!')
-
- # ## Data reconstruction using similar-day approach
- DateinDay=[]
- for k in range(DayPeriod):
- DateinDay.append(RawDate[k*DataRes_org])
- DoW, DayType = getDayType(DateinDay, DayPeriod, SpecialHoliday)
- # Find the similar-day and reconstructed data
- marking=np.nan
- ReconstructedData, DayType1h = Reconstruction(DayType, ElectricLoad_1h, marking, DataRes_24, isRecent)
-
- plt.subplot(312)
- plt.plot(ReconstructedData, '*-', label='Reconstructed data',linewidth=3)
- plt.plot(ElectricLoad_1h, '--', label='Raw data',linewidth=3)
- plt.legend(loc='upper right', fontsize = 14)
- plt.ylabel('Power [kW]', fontsize = 14)
- plt.yticks(fontsize=14)
- plt.xticks([0],fontsize=14)
- plt.xlim((DayPeriod-10)*24, DayPeriod*24)
- plt.title('Raw & reconstructed data in the latest 10 days',fontsize=14)
-
- print('Reconstruct complete!')
- # ## Day-ahead load forecasting
- ####### Convert to matrix
- ReconstructedData_Arr=np.zeros((DataRes_24, DayPeriod))
- for i in range(DayPeriod):
- if isRecent and i==DayPeriod-1:
- for j in range(len(ReconstructedData)%DataRes_24):
- ReconstructedData_Arr[j,i]=ReconstructedData[i*DataRes_24+j]
- else:
- for j in range(DataRes_24):
- ReconstructedData_Arr[j,i]=ReconstructedData[i*DataRes_24+j]
- trn_period=DayPeriod - 1
- DayType_m=np.matrix(DayType)
- Data_trn=ReconstructedData_Arr[:,0:trn_period]
- Data_tst=ReconstructedData_Arr[:,trn_period]
- DayType_trn=DayType_m[0:trn_period,:]
- DayType_tst=DayType_m[trn_period,:]
- cov_lth=np.array([int(linearFilterLength.split(',')[0]),int(linearFilterLength.split(',')[1])])
- y_pred_dayAhead = lpc_pred_DayAhead(Data_trn, DayType_trn, cov_lth, DayType_tst, DataRes_24)
- print('-------------------------Day-ahead prediction result-------------------------')
- if isRecent:
- if now.hour == 0:
- print('MAPE :', MAPE(Data_tst[0],y_pred_dayAhead[0]), 'MAE :', MAE(Data_tst[0],y_pred_dayAhead[0]))
- else:
- print('MAPE :', MAPE(Data_tst[0:now.hour],y_pred_dayAhead[0:now.hour]), 'MAE :', MAE(Data_tst[0:now.hour],y_pred_dayAhead[0:now.hour]))
- else:
- print('MAPE :', MAPE(Data_tst,y_pred_dayAhead),'MAE :', MAE(Data_tst,y_pred_dayAhead))
- print('MBE :', MBE(Data_tst,y_pred_dayAhead), 'CVRMSE :', CVRMSE(Data_tst,y_pred_dayAhead))
- print('-------------------------------------------------------------------------------')
- # ## One-step-ahead load forecasting
- y_pred_oneStep=[]
- Data_tst_oneStep=[]
- if isRecent:
- dayHour = now.hour + 1
- else:
- dayHour = DataRes_24
- for i in range(dayHour):
- ####### Convert to matrix
- ReconstructedData_tmp=ReconstructedData[i:]
- if isRecent:
- for ii in range(DataRes_24-i):
- ReconstructedData_tmp.append(np.nan)
- for ii in range(i):
- ReconstructedData_tmp.append(np.nan)
- ReconstructedData_Arr_oneStep=np.zeros((DataRes_24, DayPeriod))
- for j in range(DayPeriod):
- for k in range(DataRes_24):
- ReconstructedData_Arr_oneStep[k,j]=ReconstructedData_tmp[j*DataRes_24+k]
- Data_trn=ReconstructedData_Arr_oneStep[:,0:trn_period]
- if isRecent:
- Data_tst_oneStep.append(ReconstructedData_Arr_oneStep[i,trn_period])
- else:
- Data_tst_oneStep=ReconstructedData_Arr[:,trn_period]
- y_pred_oneStep.append(lpc_pred_OneStepAhead(Data_trn, DayType_trn, cov_lth, DayType_tst, DataRes_24))
-
- print('-------------------------OneStep-ahead prediction result-------------------------')
- if isRecent:
- if now.hour == 0:
- print('MAPE :', MAPE(Data_tst[0],y_pred_oneStep[0]), 'MAE :', MAE(Data_tst[0],y_pred_oneStep[0]))
- else:
- print('MAPE :', MAPE(Data_tst[0:now.hour],y_pred_oneStep[0:now.hour]), 'MAE :', MAE(Data_tst[0:now.hour],y_pred_oneStep[0:now.hour]))
- else:
- print('MAPE :', MAPE(Data_tst_oneStep,y_pred_oneStep),'MAE :', MAE(Data_tst_oneStep,y_pred_oneStep))
- print('-------------------------------------------------------------------------------')
- plt.subplot(313)
- plt.grid(b=True, which='both',axis='y')
- if isRecent:
- plt.plot(ReconstructedData_Arr[0:now.hour,trn_period], label='Observed data', linewidth=3)
- else:
- plt.plot(ReconstructedData_Arr[:,trn_period], label='Observed data', linewidth=3)
- plt.plot(y_pred_dayAhead, '--', label='Day-ahead Prediction', linewidth=3)
- plt.plot(y_pred_oneStep, '*-.', label='OneStep-ahead Prediction', MarkerSize=10, linewidth=3)
- plt.xlabel('Time [hour]', fontsize = 14)
- plt.ylabel('Power [kW]', fontsize = 14)
- plt.legend(loc='upper right', fontsize = 14)
- plt.xticks([6,12,18,24],['6','12','18','24'], fontsize = 14)
- plt.yticks(fontsize = 14)
- plt.ylim(min(ReconstructedData)*0.9,max(ReconstructedData)*1.1)
- if isRecent:
- plt.title("Electric load forecasting on "+str(now.year)+"."+str(now.month)+"."+str(now.day)+" (Updated every hour) - DGB 2nd branch", fontsize=14)
- else:
- plt.title("Electric load forecasting on "+str(lastday.year)+"."+str(lastday.month)+"."+str(lastday.day)+" (Updated every hour) - DGB 2nd branch", fontsize=14)
- #plt.show()
- print("=================== Prediction was successfully finished! ===================")
- fig = plt.gcf()
- if isRecent:
- # Save the figure file of result
- # fig.savefig("Result of electric load forecasting on "+str(now.year)+"."+str(now.month)+"."+str(now.day)+" "+str(now.hour)+"h"+str(now.minute)+"m - DGB 2nd branch.png", dpi=fig.dpi)
-
- ### One-hour-ahead load forecasting updated every 1 minute
- # MSSQL Access
- conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName)
- # Create Cursor from Connection
- cursor = conn.cursor()
- cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsMonitoringPointForecastingHourAhead (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,ForecastedValue) VALUES(1,99,4863,1,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','"+ datetime.datetime(now.year,now.month,now.day,now.hour,0,0).strftime('%Y-%m-%d %H:00:00') + "',"+str(y_pred_oneStep[-1])+")")
-
- ## Insert data temporary
- if now.hour==0:
- try:
- cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsMonitoringPointHistoryHourly (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,CurrentValue) VALUES(1,99,4863,1,'" + (datetime.datetime(now.year,now.month,now.day,now.hour,0,0) - datetime.timedelta(hours=1)).strftime('%Y-%m-%d %H:00:00') + "',"+str(ReconstructedData_Arr[23,trn_period-1])+")")
- conn.commit()
- except:
- print('Hour-ahead forecasted data already exists! (' + (datetime.datetime(now.year,now.month,now.day,now.hour,0,0) - datetime.timedelta(hours=1)).strftime('%Y-%m-%d %H:00:00') + ')')
-
- else:
- try:
- cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsMonitoringPointHistoryHourly (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,CurrentValue) VALUES(1,99,4863,1,'" + (datetime.datetime(now.year,now.month,now.day,now.hour,0,0) - datetime.timedelta(hours=1)).strftime('%Y-%m-%d %H:00:00') + "',"+str(ReconstructedData_Arr[now.hour-1,trn_period])+")")
- conn.commit()
- except:
- print('Hour-ahead forecasted data already exists! (' + (datetime.datetime(now.year,now.month,now.day,now.hour,0,0) - datetime.timedelta(hours=1)).strftime('%Y-%m-%d %H:00:00') + ')')
-
- ### Day-ahead load forecasting updated every midnight
- if now.hour == 0:
- # Create Cursor from Connection
- cursor = conn.cursor()
- for i in range(len(y_pred_dayAhead)):
- try:
- cursor.execute("INSERT INTO " + targetDBName + ".dbo.BemsMonitoringPointForecastingDayAhead (SiteId,FacilityTypeId,FacilityCode,PropertyId,CreatedDateTime,TargetDateTime,ForecastedValue) VALUES(1,99,4863,1,'" + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + "','" + (datetime.datetime(now.year,now.month,now.day,0,0,0) + datetime.timedelta(hours=i)).strftime('%Y-%m-%d %H:00:00') + "'," + str(y_pred_dayAhead[i]) + ")")
- conn.commit()
- except:
- print('Day-ahead forecasted data already exists! ('+(datetime.datetime(now.year,now.month,now.day,0,0,0) + datetime.timedelta(hours=i)).strftime('%Y-%m-%d %H:00:00')+')')
-
- conn.close()
- print("The result was saved!")
- else:
- fig.savefig("Result of electric load forecasting on "+str(lastday.year)+"."+str(lastday.month)+"."+str(lastday.day)+ "- DGB 2nd branch.png", dpi=fig.dpi)
- plt.show()
-
- print("Sleeping for 60 seconds ...")
-
- else:
- print("No data ... Sleeping for 60 seconds ...")
-
- time.sleep(60)
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