RealTimeSimulator_HeatStorageSystem.py 79 KB

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  1. #!/usr/bin/env python
  2. # coding: utf-8
  3. import time
  4. import datetime
  5. import numpy as np
  6. import math
  7. from korean_lunar_calendar import KoreanLunarCalendar
  8. import configparser
  9. import pymssql
  10. from sklearn import ensemble
  11. from sklearn.model_selection import train_test_split
  12. ## Measure
  13. def MAPE(y_observed, y_pred):
  14. return np.mean(np.abs((y_observed - y_pred) / y_observed)) * 100
  15. def MAE(y_observed, y_pred):
  16. return np.mean(np.abs(y_observed - y_pred))
  17. def MBE(y_observed, y_pred):
  18. return (np.sum((y_observed - y_pred))/(len(y_observed)*np.mean(y_observed)))*100
  19. def CVRMSE(y_observed, y_pred):
  20. return (np.sqrt(np.mean((y_observed - y_pred)*(y_observed - y_pred)))/np.mean(y_observed))*100
  21. def Check_AlivedTimeStamp(RawData, ComparedData, idx_raw, idx_comp, unit):
  22. if unit == 'daily':
  23. 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):
  24. isAlived = True
  25. else:
  26. isAlived = False
  27. elif unit == 'quarterly':
  28. 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):
  29. isAlived = True
  30. else:
  31. isAlived = False
  32. return isAlived
  33. def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday, Day_Period, OrgDataRes, isRecent):
  34. CumTime = datetime.datetime(int(startday.strftime('%Y')), int(startday.strftime('%m')), int(startday.strftime('%d')), 0, 0, 0)
  35. StandardTimeStamp_DayUnit = [CumTime]
  36. StandardTimeStamp_QuarterUnit = [CumTime]
  37. # Create intact time stamp
  38. for idx_day in range(Day_Period):
  39. StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
  40. if isRecent and idx_day == Day_Period-1:
  41. tmp_len = now.hour*4 + int(now.minute/15)
  42. for idx_time in range(tmp_len):
  43. CumTime += datetime.timedelta(minutes = 15)
  44. StandardTimeStamp_QuarterUnit.append(CumTime)
  45. else:
  46. for idx_time in range(OrgDataRes):
  47. CumTime += datetime.timedelta(minutes = 15)
  48. StandardTimeStamp_QuarterUnit.append(CumTime)
  49. ### Extract data within day period
  50. Raw_Date=[] # raw data (date)
  51. Raw_Value=[] # raw data (value)
  52. for i in range(len(raw_Data)):
  53. if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
  54. if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday:
  55. Raw_Date.append(raw_Data[i][4])
  56. Raw_Value.append(raw_Data[i][5])
  57. if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday:
  58. break
  59. Data_len=len(Raw_Date)
  60. if isRecent:
  61. DataAct_len = (Day_Period-1)*OrgDataRes + now.hour*4 + int(now.minute/15)+1
  62. else:
  63. DataAct_len = Day_Period*OrgDataRes
  64. ### Unknown/duplicated data counts
  65. DataCount=[]
  66. for i in range(len(StandardTimeStamp_DayUnit)):
  67. cnt_unk=0 # Unknown data count
  68. for j in range(Data_len-1):
  69. if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
  70. cnt_unk += 1
  71. if isRecent and i==len(StandardTimeStamp_DayUnit)-1:
  72. DataCount.append([StandardTimeStamp_DayUnit[i], now.hour*4 + int(now.minute/15) - cnt_unk])
  73. else:
  74. DataCount.append([StandardTimeStamp_DayUnit[i], OrgDataRes-cnt_unk])
  75. DataCountMat=np.matrix(DataCount)
  76. ######## 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
  77. data_w_nan=[]
  78. idx=0
  79. idx2=0
  80. isBadData = False
  81. for i in range(DataAct_len):
  82. 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:
  83. if isBadData == True:
  84. data_w_nan.append(np.nan)
  85. isBadData=False
  86. elif Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx, idx2, 'quarterly'):
  87. data_w_nan.append(raw_Data[idx][5])
  88. else:
  89. if i > 1:
  90. data_w_nan[-1]=np.nan
  91. data_w_nan.append(np.nan)
  92. #data_w_nan.append(np.nan)
  93. if raw_Data[idx+1][5] > 0 and Check_AlivedTimeStamp(raw_Data, StandardTimeStamp_QuarterUnit, idx+1, idx2+1, 'quarterly'):
  94. isBadData = True
  95. idx -= 1
  96. idx2 += 1
  97. idx += 1
  98. return StandardTimeStamp_QuarterUnit, data_w_nan, DataCountMat
  99. ### 예보데이터는 내일 데이터까지 확보해야하기때문에 리스트 수가 설비 데이터에 비해 하루 치가 더 많다
  100. def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday, lastday, Day_Period):
  101. StandardTimeStamp_DayUnit = []
  102. # Create intact time stamp
  103. for idx_day in range(Day_Period+1):
  104. StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
  105. ### Extract data within day period
  106. Raw_Value_max=[] # raw data (value)
  107. Raw_Value_min=[]
  108. Raw_Value_mean=[]
  109. Raw_Date=[] # raw data (date)
  110. tmp_data=[raw_Data[0][5]]
  111. for i in range(len(raw_Data)):
  112. if i == len(raw_Data)-1:
  113. Raw_Date.append(raw_Data[i][4])
  114. Raw_Value_max.append(max(tmp_data))
  115. Raw_Value_min.append(min(tmp_data))
  116. Raw_Value_mean.append(np.mean(tmp_data))
  117. elif datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
  118. if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday + datetime.timedelta(days=1):
  119. 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):
  120. Raw_Date.append(raw_Data[i][4])
  121. Raw_Value_max.append(max(tmp_data))
  122. Raw_Value_min.append(min(tmp_data))
  123. Raw_Value_mean.append(np.mean(tmp_data))
  124. tmp_data=[]
  125. tmp_data.append(raw_Data[i+1][5])
  126. if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday + datetime.timedelta(days=1):
  127. break
  128. Data_len=len(Raw_Date)
  129. ### Unknown/duplicated data counts
  130. DataCount=[]
  131. for i in range(len(StandardTimeStamp_DayUnit)):
  132. cnt_unk=0 # Unknown data count
  133. for j in range(Data_len-1):
  134. if StandardTimeStamp_DayUnit[i] == datetime.date(Raw_Date[j].year,Raw_Date[j].month,Raw_Date[j].day):
  135. cnt_unk += 1
  136. DataCount.append([StandardTimeStamp_DayUnit[i], 1-cnt_unk])
  137. DataCountMat=np.matrix(DataCount)
  138. ######## 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
  139. MaxData_w_nan=[]
  140. MinData_w_nan=[]
  141. MeanData_w_nan=[]
  142. for i in range(len(StandardTimeStamp_DayUnit)):
  143. for j in range(len(Raw_Date)):
  144. if Check_AlivedTimeStamp(Raw_Date, StandardTimeStamp_DayUnit, j, i, 'daily'):
  145. MaxData_w_nan.append(Raw_Value_max[j])
  146. MinData_w_nan.append(Raw_Value_min[j])
  147. MeanData_w_nan.append(Raw_Value_mean[j])
  148. break
  149. elif j == len(Raw_Date)-1:
  150. MaxData_w_nan.append(np.nan)
  151. MinData_w_nan.append(np.nan)
  152. MeanData_w_nan.append(np.nan)
  153. return StandardTimeStamp_DayUnit, MaxData_w_nan, MinData_w_nan, MeanData_w_nan, DataCountMat
  154. ### Define day-type
  155. def getDayName(year, month, day):
  156. return ['MON','TUE','WED','THU','FRI','SAT','SUN'][datetime.date(year, month, day).weekday()]
  157. def getDayType(DateinDay, Period, SpecialHoliday):
  158. DoW=[]; # Day of Week
  159. for i in range(Period):
  160. if DateinDay[i].year==2019 and DateinDay[i].month==5 and DateinDay[i].day==18:
  161. DoW.append([5, DateinDay[i]])
  162. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'MON':
  163. DoW.append([1, DateinDay[i]])
  164. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'TUE':
  165. DoW.append([2, DateinDay[i]])
  166. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'WED':
  167. DoW.append([3, DateinDay[i]])
  168. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'THU':
  169. DoW.append([4, DateinDay[i]])
  170. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'FRI':
  171. DoW.append([5, DateinDay[i]])
  172. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SAT':
  173. DoW.append([6, DateinDay[i]])
  174. elif getDayName(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day) == 'SUN':
  175. DoW.append([7, DateinDay[i]])
  176. for j in range(len(SpecialHoliday)):
  177. if SpecialHoliday[j] == datetime.date(DateinDay[i].year,DateinDay[i].month,DateinDay[i].day):
  178. DoW[-1][0] = 8
  179. break
  180. ### W-W:1, N-W:2, W-N:3, N-N:4 ###
  181. DayType=[]
  182. for i in range(Period):
  183. if i==0:
  184. if DoW[i][0] <= 5:
  185. DayType.append([1, DateinDay[i]])
  186. elif DoW[i][0] > 5:
  187. DayType.append([3, DateinDay[i]])
  188. else:
  189. if DoW[i-1][0] <= 5 and DoW[i][0] <= 5:
  190. DayType.append([1, DateinDay[i]])
  191. elif DoW[i-1][0] > 5 and DoW[i][0] <= 5:
  192. DayType.append([2, DateinDay[i]])
  193. elif DoW[i-1][0] <= 5 and DoW[i][0] > 5:
  194. DayType.append([3, DateinDay[i]])
  195. elif DoW[i-1][0] > 5 and DoW[i][0] > 5:
  196. DayType.append([4, DateinDay[i]])
  197. return DoW, DayType
  198. if __name__ == "__main__" :
  199. Init = True
  200. ## Check every 15min. in the infinite loop
  201. while True:
  202. now = datetime.datetime.now().now()
  203. ## distinguish real time update and specific day
  204. ## 자정에 생기는 인덱싱 문제로 0시에는 16분에 업데이트, 나머지는 15분에 한 번씩 업데이트
  205. if Init:
  206. prev_time_minute = now.minute - 1 ## 알고리즘 중복 수행 방지 (알고리즘 수행시 1분이 안걸리기에 한타임에 알고리즘 한번만 동작시키기 위함)
  207. if (now.hour != 0 and now.minute%15 == 1 and now.second > 0 and now.second < 5) and prev_time_minute != now.minute:
  208. ActiveAlgorithm = True
  209. prev_time_minute = now.minute
  210. else:
  211. ActiveAlgorithm = False
  212. if ActiveAlgorithm or Init:
  213. ## Loading .ini file
  214. myINI = configparser.ConfigParser()
  215. myINI.read("Config.ini", "utf-8" )
  216. # MSSQL Access
  217. 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)
  218. # Create Cursor from Connection
  219. cursor = conn.cursor()
  220. # Execute SQL (Electric consumption)
  221. cursor.execute('SELECT * FROM BemsConfigData where SiteId = 1')
  222. rowDB_info = cursor.fetchone()
  223. conn.close()
  224. loadDBIP = rowDB_info[1]
  225. loadDBUserID = rowDB_info[2]
  226. loadDBUserPW = rowDB_info[3]
  227. loadDBName = rowDB_info[4]
  228. targetDBIP = rowDB_info[5]
  229. targetDBUserID = rowDB_info[6]
  230. targetDBUserPW = rowDB_info[7]
  231. targetDBName = rowDB_info[8]
  232. startday = datetime.date(int(rowDB_info[9].year), int(rowDB_info[9].month), int(rowDB_info[9].day))
  233. now=datetime.datetime.now().now()
  234. lastday = datetime.date(now.year, now.month, now.day)
  235. isRecent = True
  236. if startday < datetime.date(2020,4,8):
  237. print('[ERROR] 데이터 최소 시작 시점은 2020.04.08 입니다')
  238. startday = datetime.date(2020,4,9)
  239. elif startday > lastday:
  240. print('[ERROR] 예측 타깃 시작시점이 데이터 시작 시점보다 작을 수 없습니다')
  241. ##############################################################################################
  242. ## 기온, 습도 예보 데이터 로드
  243. # MSSQL 접속
  244. conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
  245. # Connection 으로부터 Cursor 생성
  246. cursor = conn.cursor()
  247. # SQL문 실행 (기온 예보)
  248. cursor.execute('SELECT * FROM BemsMonitoringPointWeatherForecasted where SiteId = 1 and Category = '+"'"+'Temperature'+"'"+' order by ForecastedDateTime desc')
  249. row = cursor.fetchone()
  250. rawWFTemperature = [row]
  251. while row:
  252. row = cursor.fetchone()
  253. if row == None:
  254. break
  255. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  256. break
  257. rawWFTemperature.append(row)
  258. rawWFTemperature.reverse()
  259. # SQL문 실행 (습도 예보)
  260. cursor.execute('SELECT * FROM BemsMonitoringPointWeatherForecasted where SiteId = 1 and Category = '+"'"+'Humidity'+"'"+' order by ForecastedDateTime desc')
  261. row = cursor.fetchone()
  262. rawWFHumidity = [row]
  263. while row:
  264. row = cursor.fetchone()
  265. if row == None:
  266. break
  267. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  268. break
  269. rawWFHumidity.append(row)
  270. rawWFHumidity.reverse()
  271. ##############################################################################################
  272. startday = datetime.date(rawWFHumidity[0][4].year, rawWFHumidity[0][4].month, rawWFHumidity[0][4].day) ## 데이터 불러오는 DB가 선구축된다고 가정하여 예보데이터 기준으로 startday define
  273. DayPeriod = (lastday - startday).days + 1
  274. print('* StartDay :',startday,',', 'LastDay :', lastday,',','Current Time :', now, ',','Day period :', DayPeriod)
  275. # MSSQL 접속
  276. conn = pymssql.connect(host = loadDBIP, user = loadDBUserID, password = loadDBUserPW, database = loadDBName, autocommit=True)
  277. # Connection 으로부터 Cursor 생성
  278. cursor = conn.cursor()
  279. DataRes_96=96
  280. DataRes_24=24
  281. print('************ (Start) Load & pre-processing data !! ************')
  282. # SQL문 실행 (축열조 축열량)
  283. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 2 order by CreatedDateTime desc')
  284. row = cursor.fetchone()
  285. rawChillerCalAmount=[row]
  286. while row:
  287. row = cursor.fetchone()
  288. if row == None:
  289. break
  290. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  291. break
  292. rawChillerCalAmount.append(row)
  293. rawChillerCalAmount.reverse()
  294. # SQL문 실행 (축열조 제빙운전상태)
  295. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 16 order by CreatedDateTime desc')
  296. row = cursor.fetchone()
  297. rawChillerStatusIcing=[row]
  298. while row:
  299. row = cursor.fetchone()
  300. if row == None:
  301. break
  302. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  303. break
  304. rawChillerStatusIcing.append(row)
  305. rawChillerStatusIcing.reverse()
  306. # SQL문 실행 (축열조 축단운전상태)
  307. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 17 order by CreatedDateTime desc')
  308. row = cursor.fetchone()
  309. rawChillerStatusDeicing=[row]
  310. while row:
  311. row = cursor.fetchone()
  312. if row == None:
  313. break
  314. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  315. break
  316. rawChillerStatusDeicing.append(row)
  317. rawChillerStatusDeicing.reverse()
  318. # SQL문 실행 (축열조 병렬운전상태)
  319. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 18 order by CreatedDateTime desc')
  320. row = cursor.fetchone()
  321. rawChillerStatusParallel=[row]
  322. while row:
  323. row = cursor.fetchone()
  324. if row == None:
  325. break
  326. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  327. break
  328. rawChillerStatusParallel.append(row)
  329. rawChillerStatusParallel.reverse()
  330. # SQL문 실행 (축열조 냉단운전상태)
  331. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 19 order by CreatedDateTime desc')
  332. row = cursor.fetchone()
  333. rawChillerStatusRefOnly=[row]
  334. while row:
  335. row = cursor.fetchone()
  336. if row == None:
  337. break
  338. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  339. break
  340. rawChillerStatusRefOnly.append(row)
  341. rawChillerStatusRefOnly.reverse()
  342. ## 현재 2019, 2020년 냉동기 전력량 데이터가 없으므로 2년 전 데이터를 활용
  343. # SQL문 실행 (냉동기1 전력량)
  344. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 order by CreatedDateTime desc')
  345. row = cursor.fetchone()
  346. rawRefPowerConsume1=[row]
  347. while row:
  348. row = cursor.fetchone()
  349. if row == None:
  350. break
  351. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  352. break
  353. rawRefPowerConsume1.append(row)
  354. rawRefPowerConsume1.reverse()
  355. # SQL문 실행 (냉동기1 운전상태)
  356. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 order by CreatedDateTime desc')
  357. row = cursor.fetchone()
  358. rawRefStatus1=[row]
  359. while row:
  360. row = cursor.fetchone()
  361. if row == None:
  362. break
  363. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  364. break
  365. rawRefStatus1.append(row)
  366. rawRefStatus1.reverse()
  367. # SQL문 실행 (냉동기2 전력량)
  368. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 order by CreatedDateTime desc')
  369. row = cursor.fetchone()
  370. rawRefPowerConsume2=[row]
  371. while row:
  372. row = cursor.fetchone()
  373. if row == None:
  374. break
  375. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  376. break
  377. rawRefPowerConsume2.append(row)
  378. rawRefPowerConsume2.reverse()
  379. # SQL문 실행 (냉동기2 운전상태)
  380. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 order by CreatedDateTime desc')
  381. row = cursor.fetchone()
  382. rawRefStatus2=[row]
  383. while row:
  384. row = cursor.fetchone()
  385. if row == None:
  386. break
  387. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  388. break
  389. rawRefStatus2.append(row)
  390. rawRefStatus2.reverse()
  391. # SQL문 실행 (브라인 입구온도)
  392. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 4 order by CreatedDateTime desc')
  393. row = cursor.fetchone()
  394. rawBrineInletTemperature=[row]
  395. while row:
  396. row = cursor.fetchone()
  397. if row == None:
  398. break
  399. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  400. break
  401. rawBrineInletTemperature.append(row)
  402. rawBrineInletTemperature.reverse()
  403. # SQL문 실행 (브라인 출구온도)
  404. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 3 order by CreatedDateTime desc')
  405. row = cursor.fetchone()
  406. rawBrineOutletTemperature=[row]
  407. while row:
  408. row = cursor.fetchone()
  409. if row == None:
  410. break
  411. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  412. break
  413. rawBrineOutletTemperature.append(row)
  414. rawBrineOutletTemperature.reverse()
  415. # SQL문 실행 (브라인 혼합온도)
  416. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 22 order by CreatedDateTime desc')
  417. row = cursor.fetchone()
  418. rawBrineMixedTemperature=[row]
  419. while row:
  420. row = cursor.fetchone()
  421. if row == None:
  422. break
  423. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  424. break
  425. rawBrineMixedTemperature.append(row)
  426. rawBrineMixedTemperature.reverse()
  427. # SQL문 실행 (브라인 통과유량)
  428. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 3 and FacilityCode = 4478 and PropertyId = 5 order by CreatedDateTime desc')
  429. row = cursor.fetchone()
  430. rawBrineFlowAmount=[row]
  431. while row:
  432. row = cursor.fetchone()
  433. if row == None:
  434. break
  435. if datetime.date(row[4].year,row[4].month,row[4].day) < startday:
  436. break
  437. rawBrineFlowAmount.append(row)
  438. rawBrineFlowAmount.reverse()
  439. # SQL문 실행 (정기휴일)
  440. cursor.execute('SELECT * FROM CmHoliday where SiteId = 1 and IsUse = 1')
  441. # 데이타 하나씩 Fetch하여 출력
  442. row = cursor.fetchone()
  443. regularHolidayData = [row]
  444. while row:
  445. row = cursor.fetchone()
  446. if row == None:
  447. break
  448. regularHolidayData.append(row)
  449. regularHolidayData = regularHolidayData[0:-1]
  450. # SQL문 실행 (비정기휴일)
  451. cursor.execute('SELECT * FROM CmHolidayCustom where SiteId = 1 and IsUse = 1')
  452. # 데이타 하나씩 Fetch하여 출력
  453. row = cursor.fetchone()
  454. suddenHolidayData = [row]
  455. while row:
  456. row = cursor.fetchone()
  457. if row == None:
  458. break
  459. suddenHolidayData.append(row)
  460. suddenHolidayData = suddenHolidayData[0:-1]
  461. ##############################################################################################
  462. ## 현재 2019, 2020년 냉동기 전력량 데이터가 없으므로 2년 전 데이터를 활용
  463. # SQL문 실행 (냉동기1 전력량), 2018
  464. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 11 order by CreatedDateTime desc')
  465. row = cursor.fetchone()
  466. rawRefPowerConsume1_2018=[row]
  467. while row:
  468. row = cursor.fetchone()
  469. if row == None:
  470. break
  471. if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
  472. break
  473. rawRefPowerConsume1_2018.append(row)
  474. rawRefPowerConsume1_2018.reverse()
  475. # SQL문 실행 (냉동기1 운전상태)
  476. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4479 and PropertyId = 15 order by CreatedDateTime desc')
  477. row = cursor.fetchone()
  478. rawRefStatus1_2018=[row]
  479. while row:
  480. row = cursor.fetchone()
  481. if row == None:
  482. break
  483. if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
  484. break
  485. rawRefStatus1_2018.append(row)
  486. rawRefStatus1_2018.reverse()
  487. # SQL문 실행 (냉동기2 전력량)
  488. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 11 order by CreatedDateTime desc')
  489. row = cursor.fetchone()
  490. rawRefPowerConsume2_2018=[row]
  491. while row:
  492. row = cursor.fetchone()
  493. if row == None:
  494. break
  495. if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
  496. break
  497. rawRefPowerConsume2_2018.append(row)
  498. rawRefPowerConsume2_2018.reverse()
  499. # SQL문 실행 (냉동기2 운전상태)
  500. cursor.execute('SELECT * FROM BemsMonitoringPointHistory15min where SiteId=1 and FacilityTypeId = 2 and FacilityCode = 4480 and PropertyId = 15 order by CreatedDateTime desc')
  501. row = cursor.fetchone()
  502. rawRefStatus2_2018=[row]
  503. while row:
  504. row = cursor.fetchone()
  505. if row == None:
  506. break
  507. if datetime.date(row[4].year,row[4].month,row[4].day) < datetime.date(2018,1,1):
  508. break
  509. rawRefStatus2_2018.append(row)
  510. rawRefStatus2_2018.reverse()
  511. ##############################################################################################
  512. # 연결 끊기
  513. conn.close()
  514. ## 휴일 데이터 DB에서 호출
  515. # 공휴일의 음력 계산
  516. calendar_convert = KoreanLunarCalendar()
  517. SpecialHoliday = []
  518. for i in range(lastday.year-startday.year+1):
  519. for j in range(len(regularHolidayData)):
  520. if regularHolidayData[j][3] == 1:
  521. if regularHolidayData[j][1] == 12 and regularHolidayData[j][2] == 30: ## 설 하루 전 연휴 계산을 위함
  522. calendar_convert.setLunarDate(startday.year+i-1, regularHolidayData[j][1], regularHolidayData[j][2], False)
  523. 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])))
  524. else:
  525. calendar_convert.setLunarDate(startday.year+i, regularHolidayData[j][1], regularHolidayData[j][2], False)
  526. 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])))
  527. else:
  528. SpecialHoliday.append(datetime.date(startday.year+i,regularHolidayData[j][1],regularHolidayData[j][2]))
  529. for i in range(len(suddenHolidayData)):
  530. if suddenHolidayData[i][1].year >= startday.year:
  531. SpecialHoliday.append(datetime.date(suddenHolidayData[i][1].year, suddenHolidayData[i][1].month, suddenHolidayData[i][1].day))
  532. SpecialHoliday=list(set(SpecialHoliday))
  533. ##############################################################################################
  534. ChillerCalAmount_Date, ChillerCalAmount_w_nan, DataCountMat_ChillerCalAmount = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerCalAmount, startday, lastday, DayPeriod, DataRes_96, isRecent)
  535. BrineMixedTemperature_Date, BrineMixedTemperature_w_nan, DataCountMat_BrineMixedTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineMixedTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
  536. BrineInletTemperature_Date, BrineInletTemperature_w_nan, DataCountMat_BrineInletTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineInletTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
  537. BrineOutletTemperature_Date, BrineOutletTemperature_w_nan, DataCountMat_BrineOutletTemperature = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineOutletTemperature, startday, lastday, DayPeriod, DataRes_96, isRecent)
  538. BrineFlowAmount_Date, BrineFlowAmount_w_nan, DataCountMat_BrineFlowAmount = detect_unknown_duplicated_zero_data_for_faciilty(rawBrineFlowAmount, startday, lastday, DayPeriod, DataRes_96, isRecent)
  539. ChStatusIcing_Date, ChStatusIcing_w_nan, DataCountMat_ChStatusIcing = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusIcing, startday, lastday, DayPeriod, DataRes_96, isRecent)
  540. ChStatusDeicing_Date, ChStatusDeicing_w_nan, DataCountMat_ChStatusDeicing = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusDeicing, startday, lastday, DayPeriod, DataRes_96, isRecent)
  541. ChStatusParallel_Date, ChStatusParallel_w_nan, DataCountMat_ChStatusParallel = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusParallel, startday, lastday, DayPeriod, DataRes_96, isRecent)
  542. ChStatusRefOnly_Date, ChStatusRefOnly_w_nan, DataCountMat_ChStatusRefOnly = detect_unknown_duplicated_zero_data_for_faciilty(rawChillerStatusRefOnly, startday, lastday, DayPeriod, DataRes_96, isRecent)
  543. RefPowerConsume1_Date, RefPowerConsume1_w_nan, DataCountMat_RefPowerConsume1 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume1, startday, lastday, DayPeriod, DataRes_96, isRecent)
  544. RefPowerConsume2_Date, RefPowerConsume2_w_nan, DataCountMat_RefPowerConsume2 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefPowerConsume2, startday, lastday, DayPeriod, DataRes_96, isRecent)
  545. RefStatus1_Date, RefStatus1_w_nan, DataCountMat_RefStatus1 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus1, startday, lastday, DayPeriod, DataRes_96, isRecent)
  546. RefStatus2_Date, RefStatus2_w_nan, DataCountMat_RefStatus2 = detect_unknown_duplicated_zero_data_for_faciilty(rawRefStatus2, startday, lastday, DayPeriod, DataRes_96, isRecent)
  547. ##############################################################################################
  548. ## 2019, 2020년 냉동기 전력량이 없어서 2018년 데이터로 대체
  549. DayPeriod_2018 = (datetime.date(2018,12,31) - datetime.date(2018,1,1)).days + 1
  550. 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)
  551. 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)
  552. 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)
  553. 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)
  554. ################# Using the power Consumption of Refrigerator in 2018 instead of 2020 #################
  555. #### 전력 소비량 계산
  556. _st=90*96
  557. _end=195*96
  558. period_2018=(_end-_st)/96
  559. RefStatus1_2018_w_nan_tmp=RefStatus1_2018_w_nan[_st:_end]
  560. RefPowerConsume1_2018_w_nan_tmp=RefPowerConsume1_2018_w_nan[_st:_end]
  561. RefStatus2_2018_w_nan_tmp=RefStatus2_2018_w_nan[_st:_end]
  562. RefPowerConsume2_2018_w_nan_tmp=RefPowerConsume2_2018_w_nan[_st:_end]
  563. ### Estimation based on Statistical method
  564. X1 = []
  565. X2 = []
  566. Y1 = []
  567. Y2 = []
  568. TermNum = 96
  569. for i in range(TermNum, len(RefStatus1_2018_w_nan_tmp),TermNum):
  570. X1.append(RefStatus1_2018_w_nan_tmp[i-TermNum:i])
  571. X2.append(RefStatus2_2018_w_nan_tmp[i-TermNum:i])
  572. Y1.append(RefPowerConsume1_2018_w_nan_tmp[i-TermNum:i])
  573. Y2.append(RefPowerConsume2_2018_w_nan_tmp[i-TermNum:i])
  574. xTrain1, xTest1, yTrain1, yTest1 = train_test_split(X1, Y1, test_size=0.1, shuffle =False)
  575. xTrain2, xTest2, yTrain2, yTest2 = train_test_split(X2, Y2, test_size=0.1, shuffle =False)
  576. Y_tmp1=[]
  577. Y_tmp2=[]
  578. for i in range(len(xTrain1)):
  579. for j in range(TermNum):
  580. if xTrain1[i][j] == 1:
  581. Y_tmp1.append(yTrain1[i][j])
  582. if xTrain2[i][j] == 1:
  583. Y_tmp2.append(yTrain2[i][j])
  584. mean_RefConsume1=np.mean(Y_tmp1) # 냉동기1 전력량 평균
  585. mean_RefConsume2=np.mean(Y_tmp2) # 냉동기2 전력량 평균
  586. ##############################################################################################
  587. ##############################################################################################
  588. WFTemperature_Date, WFTemperatureMax_w_nan, WFTemperatureMin_w_nan, WFTemperatureMean_w_nan, DataCountMat_WFTemperature = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFTemperature, startday, lastday, DayPeriod)
  589. WFHumidity_Date, WFHumidityMax_w_nan, WFHumidityMin_w_nan, WFHumidityMean_w_nan, DataCountMat_WFHumidity = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFHumidity, startday, lastday, DayPeriod)
  590. RawDate = ChillerCalAmount_Date
  591. ## 축열조 상태 변수 - 제빙운전:10, 축단운전:20, 병렬운전:30, 냉단운전:40, OFF:0
  592. Icing=10
  593. StorageOnly=20
  594. Parallel=30
  595. ChillerOnly=40
  596. Off=0
  597. ChillerStatus=[]
  598. for i in range(len(ChStatusIcing_Date)):
  599. if ChStatusIcing_w_nan[i]==1:
  600. ChillerStatus.append(Icing)
  601. elif ChStatusDeicing_w_nan[i]==1:
  602. ChillerStatus.append(StorageOnly)
  603. elif ChStatusParallel_w_nan[i]==1:
  604. ChillerStatus.append(Parallel)
  605. elif ChStatusRefOnly_w_nan[i]==1:
  606. ChillerStatus.append(ChillerOnly)
  607. elif ChStatusIcing_w_nan[i]==0 or ChStatusDeicing_w_nan[i]==0 or ChStatusParallel_w_nan[i]==0 or ChStatusRefOnly_w_nan[i]==0:
  608. ChillerStatus.append(Off)
  609. else:
  610. ChillerStatus.append(np.nan)
  611. ## 축/방열량에 대해서 두가지 변수를 생성한다.
  612. ## 첫번쨰는 사용자에게 상대적 열량을 보여주기 위해 0 < Q < max(Q) 사이의 값으로 구성된 열량
  613. ## 두번쨰는 실질적 계산을 위해서 NaN이 포함된 날은 제외하고 학습하므로 NaN 구간의 축/방열량은 0으로 가정하고 산출
  614. ## 축적 열량의 최대치 (정격용량) = 3060 USRT (=10,924.2 kW)일 때 100%
  615. max_q_accum_kWh = 3060*3.57
  616. q_accum_kWh=[0]
  617. nan_cnt=0
  618. nan_point=[]
  619. for i in range(len(ChillerStatus)):
  620. if math.isnan(ChillerStatus[i]): # Nan의 경우 축열량을 0이라고 가정하고 진행
  621. q_accum_kWh.append(q_accum_kWh[-1])
  622. nan_cnt += 1
  623. nan_point.append(i)
  624. else:
  625. if ChillerStatus[i] == Icing and BrineInletTemperature_w_nan[i] < BrineMixedTemperature_w_nan[i]:
  626. 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]))
  627. elif ChillerStatus[i] == StorageOnly and BrineInletTemperature_w_nan[i] > BrineMixedTemperature_w_nan[i]:
  628. 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]))
  629. elif ChillerStatus[i] == Parallel and BrineInletTemperature_w_nan[i] > BrineMixedTemperature_w_nan[i]:
  630. 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]))
  631. else: #ChillerStatus[i] == Off or ChillerStatus[i] == ChillerOnly:
  632. q_accum_kWh.append(q_accum_kWh[-1])
  633. if q_accum_kWh[-1] < 0:
  634. q_accum_kWh[-1] = 0
  635. elif q_accum_kWh[-1] > max_q_accum_kWh:
  636. q_accum_kWh[-1] = max_q_accum_kWh
  637. if nan_cnt > 48:
  638. print('[Warning] Too many nan points exist (48 points sequentially)')
  639. nan_cnt = 0
  640. q_accum_kWh = q_accum_kWh[1:len(q_accum_kWh)]
  641. q_accum_percent=[]
  642. for i in range(len(q_accum_kWh)):
  643. q_accum_percent.append((q_accum_kWh[i]/max_q_accum_kWh)*100)
  644. CalAmount_prev = q_accum_percent[:len(q_accum_percent)-96] ## DB에 비어있는 이전 축열량이 있다면 채워주기 위함
  645. #################### Calculate the Gradient on Each Operation Mode ########################
  646. cnt_nan=0
  647. CalAmount_wo_nan=[]
  648. ChillerStatus_wo_nan=[]
  649. RefStatus1_wo_nan=[]
  650. RefStatus2_wo_nan=[]
  651. RefStatus_wo_nan=[]
  652. ## 1: off,off, 2: on,off, 3: on,on
  653. for i in range(len(q_accum_percent)):
  654. 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]):
  655. CalAmount_wo_nan.append(q_accum_percent[i])
  656. ChillerStatus_wo_nan.append(ChillerStatus[i])
  657. RefStatus1_wo_nan.append(RefStatus1_w_nan[i])
  658. RefStatus2_wo_nan.append(RefStatus2_w_nan[i])
  659. RefStatus_wo_nan.append(RefStatus1_w_nan[i]+RefStatus2_w_nan[i])
  660. cnt_nan=0
  661. else:
  662. CalAmount_wo_nan.append(CalAmount_wo_nan[-1])
  663. ChillerStatus_wo_nan.append(0)
  664. RefStatus1_wo_nan.append(0)
  665. RefStatus2_wo_nan.append(0)
  666. RefStatus_wo_nan.append(0)
  667. cnt_nan+=1
  668. if cnt_nan>12:
  669. cnt_nan=0
  670. # print('There are many unknown data!')
  671. # 학습용 데이터로 사용
  672. train_size = int(len(ChillerStatus_wo_nan))
  673. ## 나머지를 검증용 데이터로 사용
  674. ## test_size = len(ChillerStatus_wo_nan) - train_size
  675. trainStatus = np.array(ChillerStatus_wo_nan[0:train_size])
  676. trainCalAmount = np.array(CalAmount_wo_nan[0:train_size])
  677. trainRefStatus1 = np.array(RefStatus1_wo_nan[0:train_size])
  678. trainRefStatus2 = np.array(RefStatus2_wo_nan[0:train_size])
  679. GradientCalAmount_mode_Icing = []
  680. GradientCalAmount_mode_StorageOnly = []
  681. GradientCalAmount_mode_Parallel = []
  682. GradientCalAmount_mode_ChillerOnly = []
  683. isNan_Point = False
  684. for i in range(len(trainStatus)):
  685. for j in range(len(nan_point)):
  686. if i == nan_point[j]:
  687. isNan_Point=True
  688. break
  689. if not isNan_Point:
  690. if trainStatus[i] == Icing and trainCalAmount[i] > trainCalAmount[i-1] and trainRefStatus1[i] == 1 and trainRefStatus2[i] == 1:
  691. GradientCalAmount_mode_Icing.append(trainCalAmount[i]-trainCalAmount[i-1])
  692. elif trainStatus[i] == StorageOnly and trainCalAmount[i] < trainCalAmount[i-1]:
  693. GradientCalAmount_mode_StorageOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
  694. elif trainStatus[i] == Parallel and trainCalAmount[i] < trainCalAmount[i-1] and (trainRefStatus1[i] == 1 or trainRefStatus2[i] == 1):
  695. GradientCalAmount_mode_Parallel.append(trainCalAmount[i]-trainCalAmount[i-1])
  696. elif trainStatus[i] == ChillerOnly and trainRefStatus1[i] == 1 and trainRefStatus2[i] == 1:
  697. GradientCalAmount_mode_ChillerOnly.append(trainCalAmount[i]-trainCalAmount[i-1])
  698. isNan_Point = False
  699. GradientCalAmount_w3sigma_mode_Icing = []
  700. if len(GradientCalAmount_mode_Icing) != 0:
  701. max3sigma_mode_Icing = np.mean(GradientCalAmount_mode_Icing)+np.std(GradientCalAmount_mode_Icing)*3
  702. min3sigma_mode_Icing = np.mean(GradientCalAmount_mode_Icing)-np.std(GradientCalAmount_mode_Icing)*3
  703. GradientCalAmount_w3sigma_mode_StorageOnly = []
  704. if len(GradientCalAmount_mode_StorageOnly) != 0:
  705. max3sigma_mode_StorageOnly = np.mean(GradientCalAmount_mode_StorageOnly)+np.std(GradientCalAmount_mode_StorageOnly)*3
  706. min3sigma_mode_StorageOnly = np.mean(GradientCalAmount_mode_StorageOnly)-np.std(GradientCalAmount_mode_StorageOnly)*3
  707. GradientCalAmount_w3sigma_mode_Parallel = []
  708. if len(GradientCalAmount_mode_Parallel) != 0:
  709. max3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)+np.std(GradientCalAmount_mode_Parallel)*3
  710. min3sigma_mode_Parallel = np.mean(GradientCalAmount_mode_Parallel)-np.std(GradientCalAmount_mode_Parallel)*3
  711. GradientCalAmount_w3sigma_mode_ChillerOnly = []
  712. if len(GradientCalAmount_mode_ChillerOnly) != 0:
  713. max3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)+np.std(GradientCalAmount_mode_ChillerOnly)*3
  714. min3sigma_mode_ChillerOnly = np.mean(GradientCalAmount_mode_ChillerOnly)-np.std(GradientCalAmount_mode_ChillerOnly)*3
  715. for i in range(len(GradientCalAmount_mode_Icing)):
  716. if GradientCalAmount_mode_Icing[i] <= max3sigma_mode_Icing and GradientCalAmount_mode_Icing[i] >= min3sigma_mode_Icing:
  717. GradientCalAmount_w3sigma_mode_Icing.append(GradientCalAmount_mode_Icing[i])
  718. for i in range(len(GradientCalAmount_mode_StorageOnly)):
  719. if GradientCalAmount_mode_StorageOnly[i] <= max3sigma_mode_StorageOnly and GradientCalAmount_mode_StorageOnly[i] >= min3sigma_mode_StorageOnly:
  720. GradientCalAmount_w3sigma_mode_StorageOnly.append(GradientCalAmount_mode_StorageOnly[i])
  721. for i in range(len(GradientCalAmount_mode_Parallel)):
  722. if GradientCalAmount_mode_Parallel[i] <= max3sigma_mode_Parallel and GradientCalAmount_mode_Parallel[i] >= min3sigma_mode_Parallel:
  723. GradientCalAmount_w3sigma_mode_Parallel.append(GradientCalAmount_mode_Parallel[i])
  724. for i in range(len(GradientCalAmount_mode_ChillerOnly)):
  725. if GradientCalAmount_mode_ChillerOnly[i] <= max3sigma_mode_ChillerOnly and GradientCalAmount_mode_ChillerOnly[i] >= min3sigma_mode_ChillerOnly:
  726. GradientCalAmount_w3sigma_mode_ChillerOnly.append(GradientCalAmount_mode_ChillerOnly[i])
  727. #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))
  728. #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))
  729. print('************ (Finish) Load & pre-processing data !! ************')
  730. print('****************************************************************')
  731. #######################################################################################
  732. ############################################################################################################
  733. #################### Prediction for the Degree of Daily Deicing ############################################
  734. ## 매일 21시~21시 15분 사이에 산출 및 DB 삽입
  735. if (now.hour == 21 and (now.minute > 0 or now.minute < 16)) or Init:
  736. print('************ (Start) The Degree of Daily Deicing is being predicted!! ************')
  737. DailyDeicingAmount = []
  738. DailyDeicingAmount_kWh = []
  739. idx = 0
  740. if isRecent and now.hour < 21: ## 21시를 전, 후로 익일 예상 방냉량이 업데이트
  741. _DayPeriod = DayPeriod-1
  742. else:
  743. _DayPeriod = DayPeriod
  744. for i in range(_DayPeriod):
  745. tmpAmount = []
  746. tmpAmount_kWh = []
  747. if i == 0:
  748. time_length = 4*21 # 첫번째 날은 저녁 9시까지 방냉량만 산출
  749. else:
  750. time_length = 96
  751. for time_idx in range(time_length):
  752. if q_accum_percent[idx] > q_accum_percent[idx+1]:
  753. tmpAmount.append(q_accum_percent[idx]-q_accum_percent[idx+1])
  754. tmpAmount_kWh.append(q_accum_kWh[idx]-q_accum_kWh[idx+1])
  755. idx += 1
  756. if len(tmpAmount) > 0:
  757. DailyDeicingAmount.append(sum(tmpAmount))
  758. DailyDeicingAmount_kWh.append(sum(tmpAmount_kWh))
  759. else:
  760. DailyDeicingAmount.append(0)
  761. DailyDeicingAmount_kWh.append(0)
  762. DateinDay=[]
  763. for k in range(_DayPeriod):
  764. DateinDay.append(RawDate[k*DataRes_96])
  765. DoW, DayType = getDayType(DateinDay, _DayPeriod, SpecialHoliday)
  766. # Collect the normal data
  767. X = []
  768. Y = []
  769. _isnan = False
  770. for i in range(_DayPeriod):
  771. if DayType[i][0] < 3 and DailyDeicingAmount[i] > 0: ## 평일이면서 축열조를 가동하고 결측값이 없는 날만 추출
  772. if i == _DayPeriod-1:
  773. time_len = int(len(ChillerStatus)%96)
  774. else:
  775. time_len = DataRes_96
  776. for j in range(time_len):
  777. if math.isnan(ChillerStatus[i*DataRes_96+j]):
  778. _isnan = True
  779. if not _isnan:
  780. 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]])
  781. Y.append(DailyDeicingAmount[i])
  782. _isnan = False
  783. xTrain, xVal, yTrain, yVal = train_test_split(X, Y, test_size=0.001, shuffle = False)
  784. 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]]
  785. #MSE의 변화를 확인하기 위하여 앙상블의 크기 범위에서 랜덤 포레스트 트레이닝
  786. maeOos = []
  787. Acc_CVRMSE = []
  788. Acc_MBE = []
  789. nTreeList = range(100, 200, 50)
  790. for iTrees in nTreeList:
  791. depth = None
  792. maxFeat = np.matrix(X).shape[1] #조정해볼 것
  793. DailyDeicing_RFModel = ensemble.RandomForestRegressor(n_estimators=iTrees,
  794. max_depth=depth, max_features=maxFeat,
  795. oob_score=False, random_state=42)
  796. DailyDeicing_RFModel.fit(xTrain, yTrain)
  797. #데이터 세트에 대한 MSE 누적
  798. prediction = DailyDeicing_RFModel.predict(xVal)
  799. maeOos.append(MAE(yVal, prediction))
  800. Acc_MBE.append(MBE(yVal, prediction))
  801. Acc_CVRMSE.append(CVRMSE(np.array(yVal), np.array(prediction)))
  802. #print('prediction', prediction)
  803. #print('yVal', yVal)
  804. #print("Validation Set of MAE : ",maeOos[-1])
  805. #print("Validation Set of CVRMSE : ", CVRMSE(yVal, prediction))
  806. #print("Validation Set of Aver. CVRMSE : ", np.mean(Acc_CVRMSE))
  807. PredictedDeIcingAmount = DailyDeicing_RFModel.predict([xTomorrow_WF]) ## 학습모델을 통한 익일 방냉량 예측
  808. PredictedDeIcingAmount_Tomorrow = round(PredictedDeIcingAmount[0],6)
  809. print('####################################################')
  810. print('## Estimated daily Deicing amount = ', PredictedDeIcingAmount_Tomorrow, ' % ##')
  811. print('####################################################')
  812. #### 익일 방냉량 DB 삽입
  813. ### Day-ahead deicing amount is updated everyday
  814. # MSSQL Access
  815. conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
  816. # Create Cursor from Connection
  817. cursor = conn.cursor()
  818. if now.hour >= 21:
  819. TargetDate = datetime.datetime(now.year,now.month,now.day,21,0,0) + datetime.timedelta(days=1)
  820. else:
  821. TargetDate = datetime.datetime(now.year,now.month,now.day,21,0,0)
  822. ## Storage deicing amount
  823. 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")
  824. # 데이타 하나씩 Fetch하여 출력
  825. row = cursor.fetchone()
  826. rawData=[]
  827. while row:
  828. row = cursor.fetchone()
  829. rawData.append(row)
  830. if rawData:
  831. try:
  832. 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')+"'")
  833. print("* The prediction of Daily deicing amount was updated!! (Recommend)")
  834. except:
  835. print("[ERROR] There is an update error!! (Daily deicing amount)")
  836. else:
  837. try:
  838. 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) + ")" )
  839. print("* The prediction of daily deicing amount was inserted!! (Recommend)")
  840. except:
  841. print("[ERROR] There is an insert error!! (Daily deicing amount)")
  842. print('************ (Finish) The Degree of Daily Deicing is being predicted!! ************')
  843. print('***********************************************************************************')
  844. #######################################################################################
  845. ##################################################################################################################################################
  846. ################# Find Optimal Operating Schedule for predicted daily deicing amount #############################################################
  847. ## 15분 주기로 현상태 반영하여 업데이트
  848. print('************ (Start) Recommended operating schedule is being found!! ************')
  849. if now.hour >= 0 and now.hour < 21:
  850. simul_lth = 24*4 - (now.hour*4 + int(now.minute/15)) - 3*4 ## (15분 단위 카운트)
  851. else:
  852. simul_lth = 24*4 - (now.hour*4 +int(now.minute/15)) + 21*4
  853. # 이미 지난 시간(전날 9 pm 이후)에 대한 데이터 정리
  854. inputX_prev = ChillerStatus_wo_nan[len(ChillerStatus_wo_nan)-(96-simul_lth):len(ChillerStatus_wo_nan)]
  855. inputX_REF1_prev = RefStatus1_wo_nan[len(RefStatus1_wo_nan)-(96-simul_lth):len(RefStatus1_wo_nan)]
  856. inputX_REF2_prev = RefStatus2_wo_nan[len(RefStatus2_wo_nan)-(96-simul_lth):len(RefStatus2_wo_nan)]
  857. RecommendedCalAmount_prev = CalAmount_wo_nan[len(CalAmount_wo_nan)-(96-simul_lth):len(CalAmount_wo_nan)]
  858. print('* Current Amount : ', CalAmount_wo_nan[-1], '[%], ', 'Estimated Deicing Amount : ', PredictedDeIcingAmount_Tomorrow, '[%]')
  859. idx = 0
  860. TermNum = 96
  861. RecommendedCalAmount = [CalAmount_wo_nan[-1]]
  862. if now.hour >= 21 or now.hour < 6:
  863. while RecommendedCalAmount[-1] < PredictedDeIcingAmount_Tomorrow:
  864. idx += 1
  865. if idx >= simul_lth:
  866. print("* It should be fully operated")
  867. break
  868. inputX = []
  869. inputX_REF1 = []
  870. inputX_REF2 = []
  871. ## 단순히 심야 운전만 고려하고 축냉량 시 제빙모드와 OFF만 고려하여 시뮬레이션 (다른 모드를 추가하여 구성할 수 있음)
  872. ## Off=0, Icing = 10, StorageOnly = 20, Parallel = 30, ChillerOnly = 40
  873. ## 추천 방냉은 저녁 9시 이후부터 아침 6시 사이까지.... 중간에 사용하고 있는 부분에 대한 것은 어떻게 처리할지...고민해야함...낮에 축단운전을 하기에....
  874. for i in range(idx):
  875. inputX.append(Icing)
  876. inputX_REF1.append(1)
  877. inputX_REF2.append(1)
  878. for i in range(simul_lth-len(inputX)):
  879. inputX.append(0)
  880. inputX_REF1.append(0)
  881. inputX_REF2.append(0)
  882. RecommendedCalAmount = [CalAmount_wo_nan[-1]]
  883. for i in range(len(inputX)):
  884. if i == 1:
  885. RecommendedCalAmount = RecommendedCalAmount[-1]
  886. if inputX[i]==Icing:
  887. if inputX_REF1[i] + inputX_REF2[i]==2:
  888. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing))
  889. elif inputX_REF1[i] + inputX_REF2[i]==1:
  890. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing)/2)
  891. else:
  892. RecommendedCalAmount.append(RecommendedCalAmount[-1])
  893. elif inputX[i]==StorageOnly:
  894. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_StorageOnly))
  895. elif inputX[i]==Parallel:
  896. if inputX_REF1[i] + inputX_REF2[i]==2:
  897. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel)*2)
  898. elif inputX_REF1[i] + inputX_REF2[i]==1:
  899. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
  900. else:
  901. RecommendedCalAmount.append(RecommendedCalAmount[-1])
  902. elif inputX[i]==ChillerOnly:
  903. if inputX_REF1[i] + inputX_REF2[i]==2:
  904. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly))
  905. elif inputX_REF1[i] + inputX_REF2[i]==1:
  906. RecommendedCalAmount.append(RecommendedCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_ChillerOnly)/2)
  907. else:
  908. RecommendedCalAmount.append(RecommendedCalAmount[-1])
  909. elif inputX[i]==0:
  910. RecommendedCalAmount.append(RecommendedCalAmount[-1])
  911. ## 0이나 100을 넘어갔을 경우 보정 (현재 데이터에서 축열량은 % 단위이기 때문에)
  912. if RecommendedCalAmount[-1] >= 100:
  913. RecommendedCalAmount[-1] = 100
  914. elif RecommendedCalAmount[-1] <= 0:
  915. RecommendedCalAmount[-1] = 0
  916. #print('max.',np.max(RecommendedCalAmount[-1]))
  917. else:
  918. print("************ It is not time to operate the storage in icing mode ")
  919. if idx == 0:
  920. inputX = []
  921. inputX_REF1 = []
  922. inputX_REF2 = []
  923. RecommendedCalAmount = []
  924. for i in range(simul_lth):
  925. inputX.append(0)
  926. inputX_REF1.append(0)
  927. inputX_REF2.append(0)
  928. RecommendedCalAmount.append(CalAmount_wo_nan[-1])
  929. inputX = inputX_prev + inputX
  930. inputX_REF1 = inputX_REF1_prev + inputX_REF1
  931. inputX_REF2 = inputX_REF2_prev + inputX_REF2
  932. RecommendedCalAmount = RecommendedCalAmount_prev + RecommendedCalAmount
  933. #### 실제 및 추천 운전 스케쥴 DB 삽입
  934. #### Recommended operating schedule is updated everyday
  935. # MSSQL Access
  936. conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
  937. # Create Cursor from Connection
  938. cursor = conn.cursor()
  939. # Execute SQL
  940. if now.hour >= 21:
  941. InitDate = datetime.datetime(now.year,now.month,now.day,21,0,0)
  942. else:
  943. InitDate = datetime.datetime(now.year,now.month,now.day,21,0,0)-datetime.timedelta(days=1)
  944. ## Storage mode
  945. 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")
  946. # 데이타 하나씩 Fetch하여 출력
  947. row = cursor.fetchone()
  948. rawData=[]
  949. while row:
  950. row = cursor.fetchone()
  951. rawData.append(row)
  952. if rawData:
  953. try:
  954. for i in range(TermNum):
  955. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  956. 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')+"'")
  957. print("* The storage operating schedule was updated!! (Recommend)")
  958. except:
  959. print("[ERROR] There is an update error!! (Ice storage mode)")
  960. else:
  961. try:
  962. for i in range(TermNum):
  963. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  964. 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)" )
  965. print("* The storage operating schedule was inserted!! (Recommend)")
  966. except:
  967. print("[ERROR] There is an insert error!! (Ice storage mode)")
  968. ## REF1 status
  969. 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")
  970. # 데이타 하나씩 Fetch하여 출력
  971. row = cursor.fetchone()
  972. rawData=[]
  973. while row:
  974. row = cursor.fetchone()
  975. rawData.append(row)
  976. if rawData:
  977. try:
  978. for i in range(TermNum):
  979. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  980. 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')+"'")
  981. print("* The refrigerator1 status was updated!! (Recommend)")
  982. except:
  983. print("[Error] There is an update error!! (Recommended refrigerator1 status)")
  984. else:
  985. try:
  986. for i in range(TermNum):
  987. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  988. 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)" )
  989. print("* The refrigerator1 status was inserted!! (Recommend)")
  990. except:
  991. print("[Error] There is an insert error!! (Recommended refrigerator1 status)")
  992. ## REF1 power consume
  993. 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")
  994. # 데이타 하나씩 Fetch하여 출력
  995. row = cursor.fetchone()
  996. rawData=[]
  997. while row:
  998. row = cursor.fetchone()
  999. rawData.append(row)
  1000. if rawData:
  1001. try:
  1002. for i in range(TermNum):
  1003. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1004. if inputX_REF1[i]==1:
  1005. TmpComsume = mean_RefConsume1
  1006. else:
  1007. TmpComsume = 0
  1008. 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')+"'")
  1009. print("* The recommended refrigerator1 power was updated!! (Recommend)")
  1010. except:
  1011. print("[ERROR] There is an update error!! (Recommended refrigerator1 power)")
  1012. else:
  1013. try:
  1014. for i in range(TermNum):
  1015. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1016. if inputX_REF1[i]==1:
  1017. TmpComsume = mean_RefConsume1
  1018. else:
  1019. TmpComsume = 0
  1020. 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)" )
  1021. print("* The recommended refrigerator1 power was inserted!! (Recommend)")
  1022. except:
  1023. print("[ERROR] There is an insert error!! (Recommended refrigerator1 power)")
  1024. ## REF2 status
  1025. 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")
  1026. # 데이타 하나씩 Fetch하여 출력
  1027. row = cursor.fetchone()
  1028. rawData=[]
  1029. while row:
  1030. row = cursor.fetchone()
  1031. rawData.append(row)
  1032. if rawData:
  1033. try:
  1034. for i in range(TermNum):
  1035. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1036. 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')+"'")
  1037. print("* The refrigerator2 status was updated!! (Recommend)")
  1038. except:
  1039. print("[ERROR] There is an update error!! (Recommended refrigerator2 status)")
  1040. else:
  1041. try:
  1042. for i in range(TermNum):
  1043. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1044. 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)" )
  1045. print("* The refrigerator2 status was inserted!! (Recommend)")
  1046. except:
  1047. print("[ERROR] There is an insert error!! (Recommended refrigerator2 status)")
  1048. ## REF2 power consume
  1049. 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")
  1050. # 데이타 하나씩 Fetch하여 출력
  1051. row = cursor.fetchone()
  1052. rawData=[]
  1053. while row:
  1054. row = cursor.fetchone()
  1055. rawData.append(row)
  1056. if rawData:
  1057. try:
  1058. for i in range(TermNum):
  1059. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1060. if inputX_REF2[i]==1:
  1061. TmpComsume = mean_RefConsume2
  1062. else:
  1063. TmpComsume = 0
  1064. 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')+"'")
  1065. print("* The recommended refrigerator2 power was updated!! (Recommend)")
  1066. except:
  1067. print("[ERROR] There is an update error!! (Recommended Refrigerator2 power)")
  1068. else:
  1069. try:
  1070. for i in range(TermNum):
  1071. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1072. if inputX_REF2[i]==1:
  1073. TmpComsume = mean_RefConsume2
  1074. else:
  1075. TmpComsume = 0
  1076. 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)" )
  1077. print("* The refrigerator2 power was inserted!! (Recommend)")
  1078. except:
  1079. print("[ERROR] There is an insert error!! (Recommended Refrigerator2 power)")
  1080. ## Thermal energy amount
  1081. 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")
  1082. # 데이타 하나씩 Fetch하여 출력
  1083. row = cursor.fetchone()
  1084. rawData=[]
  1085. while row:
  1086. row = cursor.fetchone()
  1087. rawData.append(row)
  1088. if rawData:
  1089. try:
  1090. for i in range(TermNum):
  1091. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1092. 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')+"'")
  1093. print("* Thermal energy amount was updated!! (Recommend)")
  1094. except:
  1095. print("[ERROR] There is an update error!! (Recommended thermal energy amount)")
  1096. else:
  1097. try:
  1098. for i in range(TermNum):
  1099. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1100. 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)" )
  1101. print("* Thermal energy amount was inserted!! (Recommend)")
  1102. except:
  1103. print("[ERROR] There is an insert error!! (Recommended thermal energy amount)")
  1104. ## 첫 실행시에만 동작
  1105. if Init:
  1106. ## Thermal energy amount (과거 확인 후 축열량이 공백인 경우 채워주기)
  1107. CalAmount_prev_tmp = CalAmount_prev[len(CalAmount_prev)-TermNum*5:]
  1108. for d in range(5, 0, -1): # 5일전까지
  1109. InitDate_tmp = InitDate-datetime.timedelta(days=d)
  1110. for m in range(TermNum): # 1열씩 업데이트 (중간중간 공백인 경우를 고려)
  1111. TmpDate = InitDate_tmp + datetime.timedelta(minutes=m*15)
  1112. 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")
  1113. # 데이타 하나씩 Fetch하여 출력
  1114. row = cursor.fetchone()
  1115. if row:
  1116. try:
  1117. 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')+"'")
  1118. except:
  1119. print("[ERROR] There is an update error!! (Recommended thermal energy amount)")
  1120. else:
  1121. try:
  1122. 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)" )
  1123. except:
  1124. print("[ERROR] There is an insert error!! (Recommended thermal energy amount)")
  1125. conn.close()
  1126. print('************ (Finish) Recommended operating schedule is being found!! ************')
  1127. print('**********************************************************************************')
  1128. #######################################################################################
  1129. ##################################################################################################################################################
  1130. ################# Stochastic method for estimating the Variation of Ice Thermal Storage based on Operation Mode "for Simulation" #################
  1131. #### 사용자 정의 데이터를 데이터 로드
  1132. ### 계속 체킹
  1133. while True:
  1134. now_ = datetime.datetime.now().now()
  1135. ## sleep 매분 2,6,10,... 초에만 동작
  1136. if now_.second%4==2:
  1137. break
  1138. time.sleep(1)
  1139. #time.sleep(2)
  1140. #print('start time : ', now_)
  1141. # MSSQL Access
  1142. conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
  1143. # Create Cursor from Connection
  1144. cursor = conn.cursor()
  1145. # Execute SQL
  1146. 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')
  1147. row = cursor.fetchone()
  1148. conn.close()
  1149. #print('end time : ', now_)
  1150. if Init:
  1151. if row != None:
  1152. recentDateTime = row[4]
  1153. else:
  1154. recentDateTime = now_
  1155. Init = False
  1156. ActiveSimulator = False
  1157. if row != None:
  1158. if recentDateTime < row[4]:
  1159. recentDateTime = row[4]
  1160. ActiveSimulator = True
  1161. else:
  1162. ActiveSimulator = False
  1163. now_ = datetime.datetime.now().now()
  1164. if now_.second%30 > 0 and now_.second%30 < 2:
  1165. print('* Keep an eye on updating DB table every 2 seconds ... (This message appears every 30 seconds)')
  1166. if ActiveSimulator:
  1167. print('************ (Start) Simulator! ************')
  1168. time.sleep(2)
  1169. # MSSQL Access
  1170. conn = pymssql.connect(host = targetDBIP, user = targetDBUserID, password = targetDBUserPW, database = targetDBName, autocommit=True)
  1171. # Create Cursor from Connection
  1172. cursor = conn.cursor()
  1173. # Execute SQL
  1174. InitDate = datetime.datetime(now.year, now.month, now.day, now.hour, int(int(now.minute/15)*15),0)
  1175. ## Storage mode
  1176. 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")
  1177. # 데이타 한꺼번에 Fetch
  1178. rows = cursor.fetchall()
  1179. rawData_StorageMode = []
  1180. for i in rows:
  1181. rawData_StorageMode.append(i)
  1182. time.sleep(1)
  1183. ## REF1 status
  1184. 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")
  1185. # 데이타 한꺼번에 Fetch
  1186. rows = cursor.fetchall()
  1187. rawData_RefStatus1 = []
  1188. for i in rows:
  1189. rawData_RefStatus1.append(i)
  1190. # rawData_RefStatus1=rawData_RefStatus1[:len(rawData_RefStatus1)-1]
  1191. #rawData_RefStatus1=rawData_RefStatus1[:-2]
  1192. time.sleep(1)
  1193. ## REF2 status
  1194. 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")
  1195. # 데이타 한꺼번에 Fetch
  1196. rows = cursor.fetchall()
  1197. rawData_RefStatus2 = []
  1198. for i in rows:
  1199. rawData_RefStatus2.append(i)
  1200. # rawData_RefStatus2=rawData_RefStatus2[:len(rawData_RefStatus2)-1]
  1201. # rawData_RefStatus2=rawData_RefStatus2[:-2]
  1202. CustomizedStatus=[]
  1203. for i in range(len(rawData_StorageMode)):
  1204. CustomizedStatus.append(rawData_StorageMode[i][6])
  1205. CustomizedRefStatus1=[]
  1206. for i in range(len(rawData_RefStatus1)):
  1207. CustomizedRefStatus1.append(rawData_RefStatus1[i][6])
  1208. CustomizedRefStatus2 = []
  1209. for i in range(len(rawData_RefStatus2)):
  1210. CustomizedRefStatus2.append(rawData_RefStatus2[i][6])
  1211. # 한번 더 데이터 불러오기 (가끔 제대로 로드 안되는 경우 있음)
  1212. time.sleep(0.5)
  1213. if len(CustomizedStatus) != len(CustomizedRefStatus1):
  1214. ## REF1 status
  1215. 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")
  1216. # 데이타 한꺼번에 Fetch
  1217. rows = cursor.fetchall()
  1218. rawData_RefStatus1 = []
  1219. for i in rows:
  1220. rawData_RefStatus1.append(i)
  1221. CustomizedRefStatus1=[]
  1222. for i in range(len(rawData_RefStatus1)):
  1223. CustomizedRefStatus1.append(rawData_RefStatus1[i][6])
  1224. time.sleep(0.5)
  1225. if len(CustomizedStatus) != len(CustomizedRefStatus2):
  1226. ## REF2 status
  1227. 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")
  1228. # 데이타 한꺼번에 Fetch
  1229. rows = cursor.fetchall()
  1230. rawData_RefStatus2 = []
  1231. for i in rows:
  1232. rawData_RefStatus2.append(i)
  1233. CustomizedRefStatus2 = []
  1234. for i in range(len(rawData_RefStatus2)):
  1235. CustomizedRefStatus2.append(rawData_RefStatus2[i][6])
  1236. SimulCalAmount=[CalAmount_wo_nan[-1]]
  1237. for i in range(len(CustomizedStatus)):
  1238. if i == 1:
  1239. SimulCalAmount = [SimulCalAmount[-1]]
  1240. ## 제빙운전은 두대로 운영되었으므로 평균값은 2대 운전 기준
  1241. if CustomizedStatus[i] == Icing:
  1242. if len(GradientCalAmount_w3sigma_mode_Icing) == 0:
  1243. print('[Warning] There is no enough data (Icing)')
  1244. SimulCalAmount.append(SimulCalAmount[-1])
  1245. else:
  1246. if CustomizedRefStatus1[i] + CustomizedRefStatus2[i] == 2:
  1247. SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing))
  1248. elif CustomizedRefStatus1[i] + CustomizedRefStatus2[i] == 1:
  1249. SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Icing)/2)
  1250. else:
  1251. SimulCalAmount.append(SimulCalAmount[-1])
  1252. ## 축단운전은 냉동기가 운영되지 않음
  1253. elif CustomizedStatus[i] == StorageOnly:
  1254. if len(GradientCalAmount_w3sigma_mode_StorageOnly) == 0:
  1255. print('[Warning] There is no enough data (Storage Only)')
  1256. SimulCalAmount.append(SimulCalAmount[-1])
  1257. else:
  1258. SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_StorageOnly))
  1259. ## 병렬운전에서 축열조 변화량은 냉동기 상태와 상관없음
  1260. elif CustomizedStatus[i] == Parallel:
  1261. if len(GradientCalAmount_w3sigma_mode_Parallel) == 0:
  1262. print('[Warning] There is no enough data (Parallel)')
  1263. SimulCalAmount.append(SimulCalAmount[-1])
  1264. else:
  1265. SimulCalAmount.append(SimulCalAmount[-1]+np.mean(GradientCalAmount_w3sigma_mode_Parallel))
  1266. ## 냉단운전은 냉동기 두대로 운영되었으므로 축열량은 그대로
  1267. elif CustomizedStatus[i] == ChillerOnly:
  1268. if len(GradientCalAmount_w3sigma_mode_ChillerOnly) == 0:
  1269. print('[Warning] There is no enough data (Chiller Only)')
  1270. SimulCalAmount.append(SimulCalAmount[-1])
  1271. else:
  1272. SimulCalAmount.append(SimulCalAmount[-1])
  1273. elif CustomizedStatus[i]==0:
  1274. SimulCalAmount.append(SimulCalAmount[-1])
  1275. if SimulCalAmount[-1] > 100:
  1276. SimulCalAmount[-1] = 100
  1277. CustomizedRefStatus1[i] = 0
  1278. CustomizedRefStatus2[i] = 0
  1279. elif SimulCalAmount[-1] < 0:
  1280. SimulCalAmount[-1] = 0
  1281. CustomizedRefStatus1[i] = 0
  1282. CustomizedRefStatus2[i] = 0
  1283. #### 시뮬레이션 결과 데이터 DB 삽입
  1284. ## Thermal energy amount
  1285. for i in range(len(CustomizedStatus)):
  1286. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1287. 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') + "' ")
  1288. # 데이타 하나씩 Fetch하여 출력
  1289. row = cursor.fetchone()
  1290. if row:
  1291. try:
  1292. 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')+"'")
  1293. if i == len(CustomizedStatus)-1:
  1294. print("* Thermal energy amount was updated!! (Simul)")
  1295. except:
  1296. print("[ERROR] There is an update error!! (Simulated thermal energy amount)")
  1297. else:
  1298. try:
  1299. 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)" )
  1300. if i == len(CustomizedStatus)-1:
  1301. print("* Thermal energy amount was inserted!! (Simul)")
  1302. except:
  1303. print("[ERROR] There is an insert error!! (Simulated thermal energy amount)")
  1304. ## REF1 power consume
  1305. for i in range(len(CustomizedStatus)):
  1306. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1307. 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') + "' ")
  1308. if CustomizedRefStatus1[i]==1:
  1309. TmpComsume = mean_RefConsume1
  1310. else:
  1311. TmpComsume = 0
  1312. # 데이타 하나씩 Fetch하여 출력
  1313. row = cursor.fetchone()
  1314. if row:
  1315. try:
  1316. 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')+"'")
  1317. if i == len(CustomizedStatus)-1:
  1318. print("* The REF1 power comsumption was updated!! (Simul)")
  1319. except:
  1320. print("[ERROR] There is an update error!! (Simulated refrigerator1 power)")
  1321. else:
  1322. try:
  1323. 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)" )
  1324. if i == len(CustomizedStatus)-1:
  1325. print("* The REF1 power comsumption was inserted!! (Simul)")
  1326. except:
  1327. print("[ERROR] There is an insert error!! (Simulated refrigerator1 power)")
  1328. ## REF2 power consume
  1329. for i in range(len(CustomizedStatus)):
  1330. TmpDate = InitDate + datetime.timedelta(minutes=i*15)
  1331. 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') + "' ")
  1332. if CustomizedRefStatus2[i]==1:
  1333. TmpComsume = mean_RefConsume2
  1334. else:
  1335. TmpComsume = 0
  1336. # 데이타 하나씩 Fetch하여 출력
  1337. row = cursor.fetchone()
  1338. if row:
  1339. try:
  1340. 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')+"'")
  1341. if i == len(CustomizedStatus)-1:
  1342. print("* The REF2 power comsumption was updated!! (Simul)")
  1343. except:
  1344. print("[ERROR] There is an update error!! (Simulated refrigerator2 power)")
  1345. else:
  1346. try:
  1347. 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)" )
  1348. if i == len(CustomizedStatus)-1:
  1349. print("* The REF2 power comsumption was inserted!! (Simul)")
  1350. except:
  1351. print("[ERROR] There is an insert error!! (Simulated refrigerator2 power)")
  1352. conn.close()
  1353. print('************ (Finish) Simulator! ************')
  1354. print('*********************************************')
  1355. #######################################################################################