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Kanggu Park 4 years ago
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      RealTimeSimulator_LoadForecasting.py

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RealTimeSimulator_LoadForecasting.py

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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)