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@@ -23,7 +23,6 @@ def MBE(y_observed, y_pred):
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def CVRMSE(y_observed, y_pred):
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return (np.sqrt(np.mean((y_observed - y_pred)*(y_observed - y_pred)))/np.mean(y_observed))*100
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-
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def Check_AlivedTimeStamp(RawData, ComparedData, idx_raw, idx_comp, unit):
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if unit == 'daily':
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if datetime.date(RawData[idx_raw].year, RawData[idx_raw].month, RawData[idx_raw].day) == datetime.date(ComparedData[idx_comp].year, ComparedData[idx_comp].month, ComparedData[idx_comp].day):
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@@ -53,7 +52,6 @@ def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday
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for idx_time in range(OrgDataRes):
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CumTime += datetime.timedelta(minutes = 15)
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StandardTimeStamp_QuarterUnit.append(CumTime)
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-
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Raw_Date=[]
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@@ -66,7 +64,7 @@ def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday
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if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday:
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break
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- Data_len=len(Raw_Date)
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+ Data_len = len(Raw_Date)
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if isRecent:
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DataAct_len = (Day_Period-1)*OrgDataRes + now.hour*4 + int(now.minute/15)+1
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else:
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@@ -111,19 +109,25 @@ def detect_unknown_duplicated_zero_data_for_faciilty(raw_Data, startday, lastday
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return StandardTimeStamp_QuarterUnit, data_w_nan, DataCountMat
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-
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+
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+
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def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday, lastday, Day_Period):
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+ now = datetime.datetime.now().now()
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+ if now.hour > 21:
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+ Day_Period += 1
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+ lastday += datetime.timedelta(days=1)
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+
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StandardTimeStamp_DayUnit = []
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- for idx_day in range(Day_Period+1):
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+ for idx_day in range(Day_Period):
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StandardTimeStamp_DayUnit.append(startday + datetime.timedelta(days=idx_day))
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-
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+
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- Raw_Value_max=[]
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- Raw_Value_min=[]
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- Raw_Value_mean=[]
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- Raw_Date=[]
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- tmp_data=[raw_Data[0][5]]
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+ Raw_Value_max = []
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+ Raw_Value_min = []
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+ Raw_Value_mean = []
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+ Raw_Date = []
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+ tmp_data = [raw_Data[0][5]]
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for i in range(len(raw_Data)):
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if i == len(raw_Data)-1:
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Raw_Date.append(raw_Data[i][4])
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@@ -131,7 +135,7 @@ def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday
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Raw_Value_min.append(min(tmp_data))
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Raw_Value_mean.append(np.mean(tmp_data))
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elif datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) >= startday:
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- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday + datetime.timedelta(days=1):
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) <= lastday:
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if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) != datetime.date(raw_Data[i+1][4].year,raw_Data[i+1][4].month,raw_Data[i+1][4].day):
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Raw_Date.append(raw_Data[i][4])
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Raw_Value_max.append(max(tmp_data))
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@@ -139,10 +143,10 @@ def detect_unknown_duplicated_zero_data_for_WeatherForecast3h(raw_Data, startday
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Raw_Value_mean.append(np.mean(tmp_data))
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tmp_data=[]
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tmp_data.append(raw_Data[i+1][5])
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- if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday + datetime.timedelta(days=1):
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+ if datetime.date(raw_Data[i][4].year,raw_Data[i][4].month,raw_Data[i][4].day) > lastday:
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break
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-
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- Data_len=len(Raw_Date)
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+
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+ Data_len = len(Raw_Date)
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DataCount=[]
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for i in range(len(StandardTimeStamp_DayUnit)):
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@@ -688,15 +692,14 @@ if __name__ == "__main__" :
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Y_tmp2.append(yTrain2[i][j])
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mean_RefConsume1=np.mean(Y_tmp1)
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- mean_RefConsume2=np.mean(Y_tmp2)
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-
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+ mean_RefConsume2=np.mean(Y_tmp2)
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WFTemperature_Date, WFTemperatureMax_w_nan, WFTemperatureMin_w_nan, WFTemperatureMean_w_nan, DataCountMat_WFTemperature = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFTemperature, startday, lastday, DayPeriod)
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WFHumidity_Date, WFHumidityMax_w_nan, WFHumidityMin_w_nan, WFHumidityMean_w_nan, DataCountMat_WFHumidity = detect_unknown_duplicated_zero_data_for_WeatherForecast3h(rawWFHumidity, startday, lastday, DayPeriod)
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- RawDate = ChillerCalAmount_Date
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+ RawDate = ChStatusIcing_Date
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Icing=10
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