12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989910010110210310410510610710810911011111211311411511611711811912012112212312412512612712812913013113213313413513613713813914014114214314414514614714814915015115215315415515615715815916016116216316416516616716816917017117217317417517617717817918018118218318418518618718818919019119219319419519619719819920020120220320420520620720820921021121221321421521621721821922022122222322422522622722822923023123223323423523623723823924024124224324424524624724824925025125225325425525625725825926026126226326426526626726826927027127227327427527627727827928028128228328428528628728828929029129229329429529629729829930030130230330430530630730830931031131231331431531631731831932032132232332432532632732832933033133233333433533633733833934034134234334434534634734834935035135235335435535635735835936036136236336436536636736836937037137237337437537637737837938038138238338438538638738838939039139239339439539639739839940040140240340440540640740840941041141241341441541641741841942042142242342442542642742842943043143243343443543643743843944044144244344444544644744844945045145245345445545645745845946046146246346446546646746846947047147247347447547647747847948048148248348448548648748848949049149249349449549649749849950050150250350450550650750850951051151251351451551651751851952052152252352452552652752852953053153253353453553653753853954054154254354454554654754854955055155255355455555655755855956056156256356456556656756856957057157257357457557657757857958058158258358458558658758858959059159259359459559659759859960060160260360460560660760860961061161261361461561661761861962062162262362462562662762862963063163263363463563663763863964064164264364464564664764864965065165265365465565665765865966066166266366466566666766866967067167267367467567667767867968068168268368468568668768868969069169269369469569669769869970070170270370470570670770870971071171271371471571671771871972072172272372472572672772872973073173273373473573673773873974074174274374474574674774874975075175275375475575675775875976076176276376476576676776876977077177277377477577677777877978078178278378478578678778878979079179279379479579679779879980080180280380480580680780880981081181281381481581681781881982082182282382482582682782882983083183283383483583683783883984084184284384484584684784884985085185285385485585685785885986086186286386486586686786886987087187287387487587687787887988088188288388488588688788888989089189289389489589689789889990090190290390490590690790890991091191291391491591691791891992092192292392492592692792892993093193293393493593693793893994094194294394494594694794894995095195295395495595695795895996096196296396496596696796896997097197297397497597697797897998098198298398498598698798898999099199299399499599699799899910001001100210031004100510061007100810091010101110121013101410151016101710181019102010211022102310241025102610271028102910301031103210331034103510361037103810391040104110421043104410451046104710481049105010511052105310541055105610571058105910601061106210631064106510661067106810691070107110721073107410751076107710781079108010811082108310841085108610871088108910901091109210931094109510961097109810991100110111021103110411051106110711081109111011111112111311141115111611171118111911201121112211231124112511261127112811291130113111321133113411351136113711381139114011411142114311441145114611471148114911501151115211531154115511561157115811591160116111621163116411651166116711681169117011711172117311741175117611771178117911801181118211831184118511861187118811891190119111921193119411951196119711981199120012011202120312041205120612071208120912101211121212131214121512161217121812191220122112221223122412251226122712281229123012311232123312341235123612371238123912401241124212431244124512461247124812491250125112521253125412551256125712581259126012611262126312641265126612671268126912701271127212731274127512761277127812791280128112821283128412851286128712881289129012911292129312941295129612971298129913001301130213031304130513061307130813091310131113121313131413151316131713181319132013211322132313241325132613271328132913301331133213331334133513361337133813391340134113421343134413451346134713481349135013511352135313541355135613571358135913601361136213631364136513661367136813691370137113721373137413751376137713781379138013811382138313841385138613871388138913901391139213931394139513961397139813991400140114021403140414051406140714081409141014111412141314141415141614171418141914201421142214231424142514261427142814291430143114321433143414351436143714381439144014411442144314441445144614471448144914501451145214531454145514561457145814591460146114621463146414651466146714681469147014711472147314741475147614771478147914801481148214831484148514861487148814891490149114921493149414951496149714981499150015011502150315041505150615071508150915101511151215131514151515161517151815191520152115221523152415251526152715281529153015311532153315341535153615371538153915401541154215431544154515461547154815491550155115521553155415551556155715581559156015611562156315641565156615671568156915701571157215731574157515761577157815791580158115821583158415851586158715881589159015911592159315941595159615971598159916001601160216031604 |
- #!/usr/bin/env python
- # coding: utf-8
- import time
- import datetime
- import numpy as np
- import math
- from korean_lunar_calendar import KoreanLunarCalendar
- import configparser
- import pymssql
- from sklearn import ensemble
- from sklearn.model_selection import train_test_split
- ## 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
- def Check_AlivedTimeStamp(RawData, ComparedData, idx_raw, idx_comp, unit):
- if unit == 'daily':
- 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):
- isAlived = True
- else:
- isAlived = False
- elif unit == 'quarterly':
- 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):
- isAlived = True
- else:
- isAlived = False
- return isAlived
- def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday, Day_Period, OrgDataRes, isRecent):
- CumTime = datetime.datetime(int(startday.strftime('%Y')), int(startday.strftime('%m')), int(startday.strftime('%d')), 0, 0, 0)
- StandardTimeStamp_DayUnit = [CumTime]
- StandardTimeStamp_QuarterUnit = [CumTime]
- # Create intact time stamp
- for idx_day in range(Day_Period):
- StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
- if isRecent and idx_day == Day_Period-1:
- tmp_len = now.hour*4 + int(now.minute/15)
- for idx_time in range(tmp_len):
- CumTime += datetime.timedelta(minutes = 15)
- StandardTimeStamp_QuarterUnit.append(CumTime)
- else:
- for idx_time in range(OrgDataRes):
- CumTime += datetime.timedelta(minutes = 15)
- StandardTimeStamp_QuarterUnit.append(CumTime)
-
-
- ### Extract data within day period
- Raw_Date=[] # raw data (date)
- Raw_Value=[] # raw data (value)
- for i in range(len(raw_Data)):
- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday:
- Raw_Date.append(raw_Data[i][4])
- Raw_Value.append(raw_Data[i][5])
- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday:
- break
-
- Data_len=len(Raw_Date)
- if isRecent:
- DataAct_len = (Day_Period-1)*OrgDataRes + now.hour*4 + int(now.minute/15)+1
- else:
- DataAct_len = Day_Period*OrgDataRes
-
- ### Unknown/duplicated data counts
- DataCount=[]
- for i in range(len(StandardTimeStamp_DayUnit)):
- cnt_unk=0 # Unknown data count
- for j in range(Data_len-1):
- if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
- cnt_unk += 1
- if isRecent and i==len(StandardTimeStamp_DayUnit)-1:
- DataCount.append([StandardTimeStamp_DayUnit[i], now.hour*4 + int(now.minute/15) - cnt_unk])
- else:
- DataCount.append([StandardTimeStamp_DayUnit[i], OrgDataRes-cnt_unk])
-
- DataCountMat=np.matrix(DataCount)
- ######## 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
- data_w_nan=[]
- idx=0
- idx2=0
- isBadData = False
- for i in range(DataAct_len):
- 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:
- if isBadData == True:
- data_w_nan.append(np.nan)
- isBadData=False
- elif Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx, idx2, 'quarterly'):
- data_w_nan.append(raw_Data[idx][5])
- else:
- if i > 1:
- data_w_nan[-1]=np.nan
- data_w_nan.append(np.nan)
- #data_w_nan.append(np.nan)
- if raw_Data[idx+1][5] > 0 and Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx+1, idx2+1, 'quarterly'):
- isBadData = True
- idx -= 1
- idx2 += 1
- idx += 1
- return StandardTimeStamp_QuarterUnit, data_w_nan, DataCountMat
- ### 예보데이터는 내일 데이터까지 확보해야하기때문에 리스트 수가 설비 데이터에 비해 하루 치가 더 많다
- def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday, lastday, Day_Period):
- StandardTimeStamp_DayUnit = []
- # Create intact time stamp
- for idx_day in range(Day_Period+1):
- StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
-
- ### Extract data within day period
- Raw_Value_max=[] # raw data (value)
- Raw_Value_min=[]
- Raw_Value_mean=[]
- Raw_Date=[] # raw data (date)
- tmp_data=[raw_Data[0][5]]
- for i in range(len(raw_Data)):
- if i == len(raw_Data)-1:
- Raw_Date.append(raw_Data[i][4])
- Raw_Value_max.append(max(tmp_data))
- Raw_Value_min.append(min(tmp_data))
- Raw_Value_mean.append(np.mean(tmp_data))
- elif datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday + datetime.timedelta(days=1):
- 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):
- Raw_Date.append(raw_Data[i][4])
- Raw_Value_max.append(max(tmp_data))
- Raw_Value_min.append(min(tmp_data))
- Raw_Value_mean.append(np.mean(tmp_data))
- tmp_data=[]
- tmp_data.append(raw_Data[i+1][5])
- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday + datetime.timedelta(days=1):
- break
- Data_len=len(Raw_Date)
- ### Unknown/duplicated data counts
- DataCount=[]
- for i in range(len(StandardTimeStamp_DayUnit)):
- cnt_unk=0 # Unknown data count
- for j in range(Data_len-1):
- if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
- cnt_unk += 1
- DataCount.append([StandardTimeStamp_DayUnit[i], 1-cnt_unk])
- DataCountMat=np.matrix(DataCount)
- ######## 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
- MaxData_w_nan=[]
- MinData_w_nan=[]
- MeanData_w_nan=[]
- for i in range(len(StandardTimeStamp_DayUnit)):
- for j in range(len(Raw_Date)):
- if Check_AlivedTimeStamp(Raw_Date, StandardTimeStamp_DayUnit, j, i, 'daily'):
- MaxData_w_nan.append(Raw_Value_max[j])
- MinData_w_nan.append(Raw_Value_min[j])
- MeanData_w_nan.append(Raw_Value_mean[j])
- break
- elif j == len(Raw_Date)-1:
- MaxData_w_nan.append(np.nan)
- MinData_w_nan.append(np.nan)
- MeanData_w_nan.append(np.nan)
-
- return StandardTimeStamp_DayUnit, MaxData_w_nan, MinData_w_nan, MeanData_w_nan, DataCountMat
- ### 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-W:1, N-W:2, W-N:3, N-N:4 ###
- DayType=[]
- for i in range(Period):
- if i==0:
- if DoW[i][0] <= 5:
- DayType.append([1, DateinDay[i]])
- elif DoW[i][0] > 5:
- DayType.append([3, DateinDay[i]])
- else:
- if DoW[i-1][0] <= 5 and DoW[i][0] <= 5:
- DayType.append([1, DateinDay[i]])
- elif DoW[i-1][0] > 5 and DoW[i][0] <= 5:
- DayType.append([2, DateinDay[i]])
- elif DoW[i-1][0] <= 5 and DoW[i][0] > 5:
- DayType.append([3, DateinDay[i]])
- elif DoW[i-1][0] > 5 and DoW[i][0] > 5:
- DayType.append([4, DateinDay[i]])
- return DoW, DayType
- if __name__ == "__main__" :
- Init = True
- ## Check every 15min. in the infinite loop
- while True:
- now = datetime.datetime.now().now()
- ## distinguish real time update and specific day
- ## 자정에 생기는 인덱싱 문제로 0시에는 16분에 업데이트, 나머지는 15분에 한 번씩 업데이트
- if Init:
- prev_time_minute = now.minute - 1 ## 알고리즘 중복 수행 방지 (알고리즘 수행시 1분이 안걸리기에 한타임에 알고리즘 한번만 동작시키기 위함)
- if (now.hour != 0 and now.minute%15 == 1 and now.second > 0 and now.second < 5) and prev_time_minute != now.minute:
- ActiveAlgorithm = True
- prev_time_minute = now.minute
- else:
- ActiveAlgorithm = False
-
- if ActiveAlgorithm or Init:
-
- ## 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'), autocommit=True)
- # 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]
-
- startday = datetime.date(int(rowDB_info[9].year), int(rowDB_info[9].month), int(rowDB_info[9].day))
-
- now=datetime.datetime.now().now()
- lastday = datetime.date(now.year, now.month, now.day)
- isRecent = True
- if startday < datetime.date(2020,4,8):
- print('[ERROR] 데이터 최소 시작 시점은 2020.04.08 입니다')
- startday = datetime.date(2020,4,9)
- elif startday > lastday:
- print('[ERROR] 예측 타깃 시작시점이 데이터 시작 시점보다 작을 수 없습니다')
-
-
- ##############################################################################################
- ## 기온, 습도 예보 데이터 로드
- # MSSQL 접속
- conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
- # Connection 으로부터 Cursor 생성
- cursor = conn.cursor()
- # SQL문 실행 (기온 예보)
- cursor.execute('SELECT * FROM BemsMonitoringPointWeatherForecasted where SiteId = 1 and Category = '+"'"+'Temperature'+"'"+' order by ForecastedDateTime desc')
-
- row = cursor.fetchone()
- rawWFTemperature = [row]
- while row:
- row = cursor.fetchone()
- if row == None:
- break
- if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
- break
- rawWFTemperature.append(row)
- rawWFTemperature.reverse()
- # SQL문 실행 (습도 예보)
- 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('*********************************************')
- #######################################################################################
-
-
|