def getRmse(storename, forecastPnt, actPnt, avcPnt):
forecastVals = getRemcPntData(storename, forecastPnt).tolist()
actVals = getRemcPntData(storename, actPnt).tolist()
avcVals = getRemcPntData(storename, avcPnt).tolist()
rmsePerc = calcNrmsePerc(actVals, forecastVals, avcVals)
return rmsePerc
def getIstsGenSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,installed_capacity,avc_id,act_id,sch_id,type,pooling_station,gen_type
confDf['name'] = confDf['name'].str.strip()
confDf['type'] = confDf['type'].str.strip()
confDf['pooling_station'] = confDf['pooling_station'].str.strip()
confDf['gen_type'] = confDf['gen_type'].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') |
(confDf['type'] == '') |
(confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs('ists gen processing row number {0}'.format(rowIter + 2))
timeValSeries = getPntData('HRS')
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"installed_capacity": None,
"max_avc": None,
"day_max_actual": None,
"day_max_actual_time": None,
"day_min_actual": None,
"day_min_actual_time": None,
"sch_mu": None,
"act_mu": None,
"dev_mu": None,
"cuf": None
})
continue
elif not (pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName] ==
aggIdentifier]
avcPnt = ','.join(confDfForAgg['avc_id'].dropna().tolist())
actPnt = ','.join(confDfForAgg['act_id'].tolist())
schPnt = ','.join(confDfForAgg['sch_id'].tolist())
else:
avcPnt = confRow['avc_id']
actPnt = confRow['act_id']
schPnt = confRow['sch_id']
if ((avcPnt == '') or pd.isnull(avcPnt)):
maxAvc = None
else:
maxAvc = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
avcPnt).max()
dayMaxActual = getPntData(actPnt).max()
dayMaxActualTime = timeValSeries.iloc[getPntData(actPnt).idxmax()]
dayMinActual = getPntData(actPnt).min()
dayMinActualTime = timeValSeries.iloc[getPntData(actPnt).idxmin()]
schMu = getPntData(schPnt).mean() * 0.024
actMu = getPntData(actPnt).mean() * 0.024
devMu = actMu - schMu
installedCapacity = confRow['installed_capacity']
cufPerc = (actMu * 100000) / (24 * installedCapacity)
resValsList.append({
"name": confRow['name'],
"installed_capacity": installedCapacity,
"max_avc": maxAvc,
"day_max_actual": dayMaxActual,
"day_max_actual_time": dayMaxActualTime,
"day_min_actual": dayMinActual,
"day_min_actual_time": dayMinActualTime,
"sch_mu": schMu,
"act_mu": actMu,
"dev_mu": devMu,
"cuf": cufPerc
})
return pd.DataFrame(resValsList)
def getRemcRegionalR16ErrSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,r16_pnt,actual_pnt,cuf_pnt,avc_pnt,type
for stripCol in 'name,r16_pnt,actual_pnt,avc_pnt,type'.split(','):
confDf[stripCol] = confDf[stripCol].str.strip()
# initialize results
resValsList = []
# find the row index of first non dummy row
for rowIter in range(len(confDf)):
confRow = confDf.iloc[rowIter]
rowType = confRow['type']
if rowType == 'dummy':
resValsList.append({
"name": confRow['name'],
"solar": None,
"wind": None,
"combined": None
})
# get regional rows
solarConf = confDf[confDf['name'] == 'solar'].squeeze()
windConf = confDf[confDf['name'] == 'wind'].squeeze()
combinedConf = confDf[confDf['name'] == 'combined'].squeeze()
# get data values
regSolActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['actual_pnt'])
regSolR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['r16_pnt'])
regSolAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['avc_pnt'])
regWindActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['actual_pnt'])
regWindR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['r16_pnt'])
regWindAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['avc_pnt'])
regCombActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['actual_pnt'])
regCombR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['r16_pnt'])
regCombAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['avc_pnt'])
# get ISTS rows
solarConf = confDf[confDf['name'] == 'ists_solar'].squeeze()
windConf = confDf[confDf['name'] == 'ists_wind'].squeeze()
combinedConf = confDf[confDf['name'] == 'ists_combined'].squeeze()
# get data values
istsSolActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['actual_pnt'])
istsSolR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['r16_pnt'])
istsSolAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['avc_pnt'])
istsWindActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['actual_pnt'])
istsWindR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['r16_pnt'])
istsWindAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['avc_pnt'])
istsCombActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['actual_pnt'])
istsCombR16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['r16_pnt'])
istsCombAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['avc_pnt'])
# calculate the output rows for region
# calculate regional solar mape for r16
regSolR16MapePerc = calcMapePerc(regSolActVals, regSolR16Vals,
regSolAvcVals)
# calculate regional solar nrmse for r16
regSolR16NrmsePerc = calcNrmsePerc(regSolActVals, regSolR16Vals,
regSolAvcVals)
# calculate regional wind mape for r16
regWindR16MapePerc = calcMapePerc(regWindActVals, regWindR16Vals,
regWindAvcVals)
# calculate regional wind nrmse for r16
regWindR16NrmsePerc = calcNrmsePerc(regWindActVals, regWindR16Vals,
regWindAvcVals)
# calculate regional combined mape for r16
regCombR16MapePerc = calcMapePerc(regCombActVals, regCombR16Vals,
regCombAvcVals)
# calculate regional combined nrmse for r16
regCombR16NrmsePerc = calcNrmsePerc(regCombActVals, regCombR16Vals,
regCombAvcVals)
# calculate the output rows for ists
# calculate ists solar mape for r16
istsSolR16MapePerc = calcMapePerc(istsSolActVals, istsSolR16Vals,
istsSolAvcVals)
# calculate ists solar nrmse for r16
istsSolR16NrmsePerc = calcNrmsePerc(istsSolActVals, istsSolR16Vals,
istsSolAvcVals)
# calculate ists wind mape for r16
istsWindR16MapePerc = calcMapePerc(istsWindActVals, istsWindR16Vals,
istsWindAvcVals)
# calculate ists wind nrmse for r16
istsWindR16NrmsePerc = calcNrmsePerc(istsWindActVals, istsWindR16Vals,
istsWindAvcVals)
# calculate ists combined mape for r16
istsCombR16MapePerc = calcMapePerc(istsCombActVals, istsCombR16Vals,
istsCombAvcVals)
# calculate ists combined nrmse for r16
istsCombR16NrmsePerc = calcNrmsePerc(istsCombActVals, istsCombR16Vals,
istsCombAvcVals)
printWithTs(
'Processing REMC Regional R16 Error summary Section at row {0}'.format(
len(resValsList) + 1))
# create result dataframe rows
resValsList.extend([{
"name": "MAPE",
"solar": regSolR16MapePerc,
"wind": regWindR16MapePerc,
"combined": regCombR16MapePerc,
"istsSolar": istsSolR16MapePerc,
"istsWind": istsWindR16MapePerc,
"istsCombined": istsCombR16MapePerc
}, {
"name": "NRMSE",
"solar": regSolR16NrmsePerc,
"wind": regWindR16NrmsePerc,
"combined": regCombR16NrmsePerc,
"istsSolar": istsSolR16NrmsePerc,
"istsWind": istsWindR16NrmsePerc,
"istsCombined": istsCombR16NrmsePerc
}])
return pd.DataFrame(resValsList)
def getRemcIstsErrSummSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,r0_pnt,r16_pnt,actual_pnt,cuf_pnt,avc_pnt,type,pooling_station
for stripCol in 'name,r0_pnt,r16_pnt,actual_pnt,avc_pnt,type,pooling_station'.split(
','):
confDf[stripCol] = confDf[stripCol].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') |
(confDf['type'] == '') |
(confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs('REMC ISTS error summary processing row number {0}'.format(
rowIter + 2))
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"r0_mape": None,
"r0_nrmse": None,
"r16_mape": None,
"r16_nrmse": None
})
continue
elif not (pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName] ==
aggIdentifier]
avcPnt = ','.join(confDfForAgg['avc_pnt'].tolist())
actPnt = ','.join(confDfForAgg['actual_pnt'].tolist())
r0Pnt = ','.join(confDfForAgg['r0_pnt'].tolist())
r16Pnt = ','.join(confDfForAgg['r16_pnt'].tolist())
else:
avcPnt = confRow['avc_pnt']
actPnt = confRow['actual_pnt']
r0Pnt = confRow['r0_pnt']
r16Pnt = confRow['r16_pnt']
avcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
avcPnt).tolist()
r0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
r0Pnt).tolist()
r16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
r16Pnt).tolist()
actVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
actPnt).tolist()
r0MapePerc = calcMapePerc(actVals, r0Vals, avcVals)
r0NrmsePerc = calcNrmsePerc(actVals, r0Vals, avcVals)
r16MapePerc = calcMapePerc(actVals, r16Vals, avcVals)
r16NrmsePerc = calcNrmsePerc(actVals, r16Vals, avcVals)
resValsList.append({
"name": confRow['name'],
"r0_mape": r0MapePerc,
"r0_nrmse": r0NrmsePerc,
"r16_mape": r16MapePerc,
"r16_nrmse": r16NrmsePerc
})
return pd.DataFrame(resValsList)