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Edit and run



In [201]:

    
from __future__ import division

import os.path
import pandas as pd
import numpy as np
import csv
import glob

import matplotlib.pyplot as plt
import seaborn as sns

import datetime
import uuid

%matplotlib inline



In [242]:

    
header = ['SPID','SA','UOM','DIR','DATE','RS','NAICS','APCT',
 '0:00',
 '0:15',
 '0:30',
 '0:45',
 '1:00',
 '1:15',
 '1:30',
 '1:45',
 '2:00',
 '2:15',
 '2:30',
 '2:45',
 '3:00',
 '3:15',
 '3:30',
 '3:45',
 '4:00',
 '4:15',
 '4:30',
 '4:45',
 '5:00',
 '5:15',
 '5:30',
 '5:45',
 '6:00',
 '6:15',
 '6:30',
 '6:45',
 '7:00',
 '7:15',
 '7:30',
 '7:45',
 '8:00',
 '8:15',
 '8:30',
 '8:45',
 '9:00',
 '9:15',
 '9:30',
 '9:45',
 '10:00',
 '10:15',
 '10:30',
 '10:45',
 '11:00',
 '11:15',
 '11:30',
 '11:45',
 '12:00',
 '12:15',
 '12:30',
 '12:45',
 '13:00',
 '13:15',
 '13:30',
 '13:45',
 '14:00',
 '14:15',
 '14:30',
 '14:45',
 '15:00',
 '15:15',
 '15:30',
 '15:45',
 '16:00',
 '16:15',
 '16:30',
 '16:45',
 '17:00',
 '17:15',
 '17:30',
 '17:45',
 '18:00',
 '18:15',
 '18:30',
 '18:45',
 '19:00',
 '19:15',
 '19:30',
 '19:45',
 '20:00',
 '20:15',
 '20:30',
 '20:45',
 '21:00',
 '21:15',
 '21:30',
 '21:45',
 '22:00',
 '22:15',
 '22:30',
 '22:45',
 '23:00',
 '23:15',
 '23:30',
 '23:45',
 'ZIPCODE',
 'SUBLAP']



In [203]:

    
df = pd.read_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/95202.csv',header=None)
df.drop(df.columns[[0]], inplace=True, axis=1)
df.columns = header

df.DATE = pd.to_datetime(df.DATE, format='%m/%d/%Y')
df.index = pd.to_datetime(df.DATE, format='%m/%d/%Y')



In [204]:

    
customers = pd.read_csv('../../2016/PGE/D1977_OFFICE_LBNL.csv',delimiter=',')
# customers.drop(customers.columns[[0]], inplace=True, axis=1)



In [159]:

    
with open('/Users/ryin/Dropbox/LBNL/PGE/stockton/saids.csv','w') as csvfile:
    writer = csv.writer(csvfile,delimiter=',')
    header = ['SAID','StartDate','EndDate','ZIPCODE','SUBLAP']
    writer.writerow(header)
    
    for said in set(df.SA):
        df2 = df[df['SA'] == said]
        writer.writerow([said,df2.DATE.min().strftime('%Y-%m-%d'),df2.DATE.max().strftime('%Y-%m-%d'), df2.ZIPCODE[0],df2.SUBLAP[0]])

customer_output = '/Users/ryin/Dropbox/LBNL/PGE/stockton/customers_95202.csv'
if os.path.exists(customer_output):
    os.remove(customer_output)
    
with open(customer_output,'a') as csvfile:
    writer = csv.writer(csvfile,delimiter=',')
    writer.writerow(customers.columns)

    for said in set(df.SA):
        df_said = customers[customers['sa_id'] == said]
        df_said.to_csv(csvfile, header=False, index=False)
    print('Customer Output is Completed.')









    



Customer Output is Completed.



In [160]:

    
def str2float(x):
    try:
        return("{0:.3f}".format(float(x)))
    except ValueError:
        return np.nan

Start the analyze the dataset



In [161]:

    
def CleanMeterData(df, SAID, weather, holidays):
    df2 = df[df['SA'] == SAID].sort_index(axis=0)
    df3 = pd.DataFrame(df2[df2.columns[8:104]].stack(),columns=['energykWh'])
    df3.index.names = ['Date','Time']

    dateindex = []
    for datenum in range(len(df3)):
        dateindex.append(df3.index[datenum][0].strftime('%Y-%m-%d')+' '+df3.index[datenum][1])

    df3.index = pd.to_datetime(dateindex, format='%Y-%m-%d %H:%M')
    
    df3['power'] = df3['energykWh'].apply(lambda x: str2float(x))
    df3[['power']] = df3[['power']].astype(float)
    df3.power = df3.power * 4 # convert kWh to kW

    df3['day'] = list(map(lambda x: x.strftime('%Y/%m/%d'),df3.index))
    df3['time'] = list(map(lambda x: x.strftime('%H:%M'),df3.index))
    df3['month'] = list(map(lambda x: x.strftime('%m'),df3.index))
    df3['hour'] = list(map(lambda x: x.strftime('%H'),df3.index))
    df3['month'] = df3.month.astype(int)
    df3['hour'] = df3.hour.astype(int)
    df3['weekday'] = df3.index.weekday
    df3['holiday'] = list(map(lambda x: x.strftime('%Y-%m-%d') in holidays.day.values,df3.index))
    df3['holiday'] = df3.holiday.astype(int)
    
    data = pd.concat([df3,weather],axis=1,join_axes=[df3.index])
    
    data.fillna(method='pad')

    # calculate basepower on weekdays
    data.loc[:,'basepower'] = 0
    data.loc[:,'peakoat'] = 0

    # calculate the daily peak OAT
    PeakOAT = data.groupby(['day'])['oat'].max()

    for day in PeakOAT.index:
        data.loc[(data['day'] == day),'peakoat'] = PeakOAT[day]

    # subset the weekday and weekend power
    df_wd = data.loc[(data['weekday'] >= 0) & (data['weekday'] <= 4) & (data['holiday'] == 0)]
    df_wk = data.loc[(data.weekday.apply(lambda x: x in [5,6])) | (data['holiday'] == 1)]

    # calculate the basepower on weekdays and weekend
    df_wd_base = df_wd.loc[(df_wd['peakoat'] < 45)]
    df_wd_basedaily = df_wd_base.pivot(index='time',columns='day',values='power')

    for tim in df_wd_basedaily.index:
        data.loc[(data['time'] == tim) & (data['weekday'] >= 0) & \
           (data['weekday'] <= 4) & (data['holiday'] == 0),'basepower'] = df_wd_basedaily.mean(axis=1)[tim]
        data.loc[(data['time'] == tim) & (data.weekday.apply(lambda x: x in [5,6])),'basepower'] = df_wd_basedaily.mean(axis=1)[tim]
        data.loc[(data['time'] == tim) & (data['holiday'] == 1),'basepower'] = df_wd_basedaily.mean(axis=1)[tim]

    data.loc[:,'hvac'] = data.power - data.basepower
    
    return data



In [162]:

    
# consider the holidays
holidays = pd.read_csv('holiday.csv', usecols=[0])
holidays['date'] = pd.to_datetime(pd.Series(holidays['date']), format='%m/%d/%y')
holidays['day'] = holidays.date.apply(lambda x: x.strftime('%Y-%m-%d'))



In [163]:

    
weather = pd.read_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/stockon/KCASTOCK9.temperatureinterpolated.csv',header=None)
weather.columns = ['Date','oat']
weather.index = pd.to_datetime(weather.Date, format='%m/%d/%Y %H:%M')
weather = weather.drop(['Date'],axis=1)
weather = weather.groupby(weather.index).first() # remove duplicated Index in DataFrame
weather = weather.asfreq('15min', method='pad') # fill missing timeseries data in DataFrame



In [165]:

    
sns.set_context("poster")
# sns.set(style="darkgrid", color_codes=True)

# tips = sns.load_dataset("tips")
g = sns.jointplot("oat", "hvac", data=data, kind="reg", color="r", size=7)









    



/Users/ryin/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

2. Generate all the basic metric from meter data and weather data



In [245]:

    
def calculateCustMetrics(df, customers, SAID):
    
    df_wd = df.loc[(df['weekday'] >= 0) & (df['weekday'] <= 4) & (df['holiday'] == 0)]
    df_cssb = df_wd.loc[(df_wd['month'] >= 5) & (df_wd['month'] <= 10),['power','hour','time','oat']]
    CSSB = df_cssb.groupby(['time'])['power'].mean()

    hotDays = df_wd.groupby(['day'])['oat'].max().sort_values(ascending=False)[0:10]
    hotDaysPower = df.loc[saidData['day'].isin(hotDays.index),['power', 'oat','time','day']]
    hotDaysHVAC = df.loc[saidData['day'].isin(hotDays.index),['hvac', 'oat','time','day']]

    custHVAC = hotDaysHVAC.pivot(index='time',columns='day',values='hvac')
    custHVAC.columns = ['hotDayHVAC0','hotDayHVAC1','hotDayHVAC2','hotDayHVAC3','hotDayHVAC4',
                        'hotDayHVAC5','hotDayHVAC6','hotDayHVAC7','hotDayHVAC8','hotDayHVAC9']
    custHVAC['hotDayHVAC'] = hotDaysHVAC.groupby(['time'])['hvac'].mean()

    custMetrics = hotDaysPower.pivot(index='time',columns='day',values='power')
    custMetrics.columns = ['hotDay0','hotDay1','hotDay2','hotDay3','hotDay4',
                           'hotDay5','hotDay6','hotDay7','hotDay8','hotDay9']
    custMetrics['cssb'] = df_cssb.groupby(['time'])['power'].mean()
    custMetrics['hotDay'] = hotDaysPower.groupby(['time'])['power'].mean()
    custMetrics['peakDay'] = df.loc[df['day'] == df.power.idxmax().strftime('%Y/%m/%d'),'power'].values

    DR_Equations = pd.read_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/DREquations.csv')
    DR_Equations.index = DR_Equations.DR_Controls

    custOAT = hotDaysPower.pivot(index='time',columns='day',values='oat')
    custOAT.columns = ['hotDayOAT0','hotDayOAT1','hotDayOAT2','hotDayOAT3','hotDayOAT4','hotDayOAT5','hotDayOAT6',
                       'hotDayOAT7','hotDayOAT8','hotDayOAT9']
    custOAT['cssb_oat'] = df_cssb.groupby(['time'])['oat'].mean()
    custOAT['hotDay_oat'] = hotDaysPower.groupby(['time'])['oat'].mean()
    custOAT['peakDay_oat'] = df.loc[df['day'] == df.power.idxmax().strftime('%Y/%m/%d'),'oat'].values
    custOAT['hour'] = df.loc[df['day'] == df.power.idxmax().strftime('%Y/%m/%d'),'hour'].values
    custOAT['NoPrecool_Reset2F_alpha'] = np.nan
    custOAT['NoPrecool_Reset2F_beta'] = np.nan
    custOAT['NoPrecool_Reset4F_alpha'] = np.nan
    custOAT['NoPrecool_Reset4F_beta'] = np.nan
    custOAT['NoPrecool_Reset6F_alpha'] = np.nan
    custOAT['NoPrecool_Reset6F_beta'] = np.nan
    custOAT['Precool2F_Reset2F_alpha'] = np.nan
    custOAT['Precool2F_Reset2F_beta'] = np.nan
    custOAT['Precool2F_Reset4F_alpha'] = np.nan
    custOAT['Precool2F_Reset4F_beta'] = np.nan
    custOAT['Precool2F_Reset6F_alpha'] = np.nan
    custOAT['Precool2F_Reset6F_beta'] = np.nan

    for hour in range(14,18):
        custOAT.loc[custOAT['hour'] == hour, ['NoPrecool_Reset2F_alpha','NoPrecool_Reset2F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 0) & (DR_Equations['reset'] == 2), ['alpha_2','beta_2']].values
        custOAT.loc[custOAT['hour'] == hour, ['NoPrecool_Reset4F_alpha','NoPrecool_Reset4F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 0) & (DR_Equations['reset'] == 4), ['alpha_2','beta_2']].values
        custOAT.loc[custOAT['hour'] == hour, ['NoPrecool_Reset6F_alpha','NoPrecool_Reset6F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 0) & (DR_Equations['reset'] == 6), ['alpha_2','beta_2']].values

        custOAT.loc[custOAT['hour'] == hour, ['Precool2F_Reset2F_alpha','Precool2F_Reset2F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 2) & (DR_Equations['reset'] == 2), ['alpha_2','beta_2']].values
        custOAT.loc[custOAT['hour'] == hour, ['Precool2F_Reset4F_alpha','Precool2F_Reset4F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 2) & (DR_Equations['reset'] == 4), ['alpha_2','beta_2']].values
        custOAT.loc[custOAT['hour'] == hour, ['Precool2F_Reset6F_alpha','Precool2F_Reset6F_beta']] = \
            DR_Equations.loc[(DR_Equations['hour'] == hour) & (DR_Equations['precool'] == 2) & (DR_Equations['reset'] == 6), ['alpha_2','beta_2']].values

    result = pd.concat([custMetrics, custOAT, custHVAC], axis=1)

    usrInput = customers[customers['sa_id'] == SAID]
    usrOutput = pd.DataFrame()

    usrOutput['SAID'] = usrInput.sa_id
    usrOutput['UUID'] = uuid.uuid4()
    usrOutput.index = usrOutput['UUID']
    usrOutput['Peak_kW'] = df.power.max()
    usrOutput['Peak_kW_timestamp'] = df.power.idxmax()
    if df.power.max() >=200:
        usrOutput['CustSize'] = 'Large'
    else:
        usrOutput['CustSize'] = 'Small/Medium'
    usrOutput['NAICS'] = usrInput.sa_naics_cd.values
    usrOutput['NAICS_desc'] = usrInput.naics_desc.values
    usrOutput['cty'] = usrInput.cty_nm.values
    usrOutput['ZIPCODE'] = usrInput.zip_5_dgt.values
    usrOutput['SUBLAP'] = 'SLAP_PGSN-APND'
    usrOutput['PGE_CZ'] = 5
    usrOutput['CEC_CZ'] = 3
    usrOutput['AMP'] = usrInput.AMP.values
    usrOutput['PDP'] = usrInput.PDP.values
    usrOutput['DBP'] = usrInput.DBP.values
    usrOutput['CBP'] = usrInput.CBP.values
    usrOutput['PDP_TOU_Ratio'] = 1.0

    usrOutput['DR_Capacity_Precool0F_Reset2F'] = np.mean(result.peakDay * (result.NoPrecool_Reset2F_beta + result.peakDay_oat * result.NoPrecool_Reset2F_alpha)/100)
    usrOutput['DR_Capacity_Precool0F_Reset4F'] = np.mean(result.peakDay * (result.NoPrecool_Reset4F_beta + result.peakDay_oat * result.NoPrecool_Reset4F_alpha)/100)
    usrOutput['DR_Capacity_Precool0F_Reset6F'] = np.mean(result.peakDay * (result.NoPrecool_Reset6F_beta + result.peakDay_oat * result.NoPrecool_Reset6F_alpha)/100)
    usrOutput['DR_Capacity_Precool2F_Reset2F'] = np.mean(result.peakDay * (result.Precool2F_Reset2F_beta + result.peakDay_oat * result.Precool2F_Reset2F_alpha)/100)
    usrOutput['DR_Capacity_Precool2F_Reset4F'] = np.mean(result.peakDay * (result.Precool2F_Reset4F_beta + result.peakDay_oat * result.Precool2F_Reset4F_alpha)/100)
    usrOutput['DR_Capacity_Precool2F_Reset6F'] = np.mean(result.peakDay * (result.Precool2F_Reset6F_beta + result.peakDay_oat * result.Precool2F_Reset6F_alpha)/100)

    usrOutput['DR_Capacity_CycleOnOff_30Pct'] = np.mean(result['14:00':'17:45'].hotDayHVAC) * 0.3
    usrOutput['DR_Capacity_CycleOnOff_50Pct'] = np.mean(result['14:00':'17:45'].hotDayHVAC) * 0.5
    usrOutput['DR_Capacity_CycleOnOff_100Pct'] = np.mean(result['14:00':'17:45'].hotDayHVAC) * 1.0
    usrOutput['HVAC_Building_Ratio'] = usrOutput['DR_Capacity_CycleOnOff_100Pct'] / usrOutput['Peak_kW']

#     usrOutput.to_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/CustSummary.csv')
    return result, usrOutput



In [226]:

    
ZipCodeGroup = pd.read_csv('/Users/ryin/Dropbox/LBNL/PGE/ADRGEOOfficeZips.csv')



In [246]:

    
for zc in ZipCodeGroup[ZipCodeGroup['City']=='Stockton']['Zip5'][0:7]:
    df = pd.read_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/'+str(zc)+'.csv',header=None)
    df.drop(df.columns[[0]], inplace=True, axis=1)
    df.columns = header

    df.DATE = pd.to_datetime(df.DATE, format='%m/%d/%Y')
    df.index = pd.to_datetime(df.DATE, format='%m/%d/%Y')
    
    with open('/Users/ryin/Dropbox/LBNL/PGE/stockton/CustSummary_'+str(zc)+'.csv','w') as csvfile:
        writer = csv.writer(csvfile,delimiter=',')
        csvheader = ['UUID','SAID','UUID','Peak_kW','Peak_kW_timestamp','CustSize','NAICS',
                  'NAICS_desc','cty','ZIPCODE','SUBLAP','PGE_CZ','CEC_CZ','AMP','PDP','DBP','CBP','PDP_TOU_Ratio',
                  'DR_Capacity_Precool0F_Reset2F','DR_Capacity_Precool0F_Reset4F','DR_Capacity_Precool0F_Reset6F',
                  'DR_Capacity_Precool2F_Reset2F','DR_Capacity_Precool2F_Reset4F','DR_Capacity_Precool2F_Reset6F',
                  'DR_Capacity_CycleOnOff_30Pct','DR_Capacity_CycleOnOff_50Pct','DR_Capacity_CycleOnOff_100Pct',
                  'HVAC_Building_Ratio']
        writer.writerow(csvheader)

    for SAID in set(df.SA):
            saidData = CleanMeterData(df, SAID, weather, holidays)
            metrics = pd.DataFrame()
            custSummary = pd.DataFrame()
            metrics, custSummary = calculateCustMetrics(saidData, customers, SAID)
            metrics.to_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/CustMetrics_'+str(zc)+'.csv',mode='w',sep=',',header=True)
            custSummary.to_csv('/Users/ryin/Dropbox/LBNL/PGE/stockton/CustSummary_'+str(zc)+'.csv',mode='a',sep=',',header=False)
            print('Customer ' + str(SAID)+' is Completed.')









    



/Users/ryin/anaconda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)






    



Customer 838288097.0 is Completed.
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Customer 2117937756.0 is Completed.






    



/Users/ryin/anaconda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (17,18,19,20,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)






    



Customer 1150280962.0 is Completed.
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Customer 1018702332.0 is Completed.






    



/Users/ryin/anaconda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)






    



Customer 1477629856.0 is Completed.
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/Users/ryin/anaconda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (45,46,47,48,49,50,51,52) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)






    



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