-
Notifications
You must be signed in to change notification settings - Fork 0
/
hourly to daily meter.py
311 lines (265 loc) · 10.4 KB
/
hourly to daily meter.py
1
2
3
4
5
6
7
8
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
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
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
# -*- coding: utf-8 -*-
import pandas as pd
temp = pd.read_csv('Temp Data.csv')
temp = temp.astype({'ymdtc': str})
temp['date_s'] = temp['ymdtc'].apply(lambda x:x[:8])
temp_avg = temp.groupby(['date_s']).mean()
temp_avg = temp_avg['Temp_in_Fahrenheit']
calendar = pd.read_csv('Calender 2015-2019.csv')
calendar['date_s'] = calendar['date_s'].apply(lambda x:str(x))
f = pd.read_stata('control2015.dta')
#f.columns
f.drop(columns = ['LOCATN_K','MTR_NB','CHNM'], inplace = True)
f = f[f['he01'] != 'NULL']
f = f[f['he02'] != 'NULL']
f = f[f['he03'] != 'NULL']
f = f[f['he04'] != 'NULL']
f = f[f['he05'] != 'NULL']
f = f[f['he06'] != 'NULL']
f = f[f['he07'] != 'NULL']
f = f[f['he08'] != 'NULL']
f = f[f['he09'] != 'NULL']
f = f[f['he10'] != 'NULL']
f = f[f['he11'] != 'NULL']
f = f[f['he12'] != 'NULL']
f = f[f['he13'] != 'NULL']
f = f[f['he14'] != 'NULL']
f = f[f['he15'] != 'NULL']
f = f[f['he16'] != 'NULL']
f = f[f['he17'] != 'NULL']
f = f[f['he18'] != 'NULL']
f = f[f['he19'] != 'NULL']
f = f[f['he20'] != 'NULL']
f = f[f['he21'] != 'NULL']
f = f[f['he22'] != 'NULL']
f = f[f['he23'] != 'NULL']
f = f[f['he24'] != 'NULL']
f = f.astype({'he01':float,'he02':float,'he03':float,'he04':float,'he05':float,'he06':float,'he07':float,'he08':float,'he09':float,'he10':float,'he11':float,'he12':float,'he13':float,'he14':float,'he15':float,'he16':float,'he17':float,'he18':float,'he19':float,'he20':float,'he21':float,'he22':float,'he23':float,'he24':float})
f['he_d'] = f['he01'] + f['he02'] +f['he03'] +f['he04'] +f['he05'] +f['he06'] +f['he07'] +f['he08'] +f['he09'] +f['he10'] +f['he11'] +f['he12'] +f['he13'] +f['he14'] +f['he15'] +f['he16'] +f['he17'] +f['he18'] +f['he19'] +f['he20'] +f['he21'] +f['he22'] +f['he23'] +f['he24']
f['date_s'] = f['DATE'].apply(lambda x: x[:4] + x[5:7] + x[8:])
f.drop(columns = ['he01','he02','he03','he04','he05','he06','he07','he08','he09','he10','he11','he12','he13','he14','he15','he16','he17','he18','he19','he20','he21','he22','he23','he24',], inplace = True)
f.drop(columns = ['DATE'], inplace = True)
f = f.set_index('date_s').join(temp_avg)
f.rename({'Temp_in_Fahrenheit':'temp_avg'}, axis = 1, inplace = True)
f.reset_index(inplace = True)
f['month'] = f['date_s'].apply(lambda x: x[4:6])
f = f.astype({'month':int})
f['summer'] = f['month'].apply(lambda x: 1 if (x in [5,6,9,10]) else 0)
f['winter'] = f['month'].apply(lambda x: 1 if (x in [11,12,1,2,3,4]) else 0)
f['summer_peak'] = f['month'].apply(lambda x: 1 if (x in [7,8]) else 0)
f_s = f[f['summer'] == 1]
f_w = f[f['winter'] == 1]
f_sp = f[f['summer_peak'] == 1]
f = f[['date_s', 'BILACCT_K', 'RATE', 'he_d', 'temp_avg', 'month', 'summer', 'winter', 'summer_peak']]
f = f[f['RATE'] != 'NU']
f = f.set_index('date_s').join(calendar.set_index('date_s'))
f.drop(columns= ['Day Number'], inplace = True)
f.reset_index(inplace = True)
f['month'] = f['month'].apply(lambda x:int(x))
f = pd.get_dummies(f, columns = ['dow'], drop_first = False, prefix = 'dow')
f = pd.get_dummies(f, columns = ['month'], drop_first = False, prefix = 'month')
f['month'] = f['date_s'].apply(lambda x:x[4:6])
f['month'] = f['month'].apply(lambda x:int(x))
def holiday(x):
if x == '20150525':
return 1
elif x == '20150907':
return 1
elif x == '20151126':
return 1
elif x == '20150101':
return 1
elif x == '20150704':
return 1
elif x == '20151225':
return 1
elif x == '20160530':
return 1
elif x == '20160905':
return 1
elif x == '20161124':
return 1
elif x == '20160101':
return 1
elif x == '20160704':
return 1
elif x == '20161225':
return 1
elif x == '20170529':
return 1
elif x == '20170904':
return 1
elif x == '20171123':
return 1
elif x == '20170102':
return 1
elif x == '20170704':
return 1
elif x == '20171225':
return 1
elif x == '20180528':
return 1
elif x == '20180903':
return 1
elif x == '20181128':
return 1
elif x == '20180101':
return 1
elif x == '20180704':
return 1
elif x == '20181225':
return 1
elif x == '20190101':
return 1
else:
return 0
f['holiday'] = f['date_s'].apply(holiday)
f['temp_avg_sq'] = f['temp_avg']*f['temp_avg']
f = f.astype({'month':int})
f = f.astype({'weekend':int})
f = f.astype({'RATE':str})
f = f.astype({'holiday':int})
f.columns
f['On-peak D'] = f['weekend'] + f['holiday']
f['On-peak Day'] = f['On-peak D'].apply(lambda x:0 if (x >= 1) else 1)
f['Off-peak Day'] = f['On-peak D'].apply(lambda x:1 if (x >= 1) else 0)
f['W_On-peak Day'] = f['winter']*f['On-peak Day']
f['W_Off-peak Day'] = f['winter']*f['Off-peak Day']
f['S_On-peak Day'] = f['summer']*f['On-peak Day']
f['S_Off-peak Day'] = f['summer']*f['Off-peak Day']
f['SP_On-peak Day'] = f['summer_peak']*f['On-peak Day']
f['SP_Off-peak Day'] = f['summer_peak']*f['Off-peak Day']
f['W_On-peak Day_s'] = f['W_On-peak Day'].apply(lambda x: 'W_On-peak' if (x==1) else '')
f['W_Off-peak Day_s'] = f['W_Off-peak Day'].apply(lambda x: 'W_Off-peak' if (x==1) else '')
f['S_On-peak Day_s'] = f['S_On-peak Day'].apply(lambda x: 'S_On-peak' if (x==1) else '')
f['S_Off-peak Day_s'] = f['S_Off-peak Day'].apply(lambda x: 'S_Off-peak' if (x==1) else '')
f['SP_On-peak Day_s'] = f['SP_On-peak Day'].apply(lambda x: 'SP_On-peak' if (x==1) else '')
f['SP_Off-peak Day_s'] = f['SP_Off-peak Day'].apply(lambda x: 'SP_Off-peak' if (x==1) else '')
#f[['date_s','S_Off-peak Day_s','S_On-peak Day_s']].iloc[20000:20050]
f['c_peak'] = f['W_On-peak Day_s'] + f['W_Off-peak Day_s'] + f['S_On-peak Day_s'] + f['S_Off-peak Day_s'] + f['SP_On-peak Day_s'] + f['SP_Off-peak Day_s']
f['RATE_peak'] = f['c_peak'] + f['RATE']
#f.groupby(['RATE_peak']).count()
br_21_s_onpd = round(((3/24)*0.3022) + ((21/24)*0.0829), 4)
br_21_s_offpd = 0.0829
br_21_sp_onpd = round(((3/24)*0.3577) + ((21/24)*0.0853), 4)
br_21_sp_offpd = 0.0853
br_21_w_onpd = round(((3/24)*0.1215) + ((21/24)*0.0758), 4)
br_21_w_offpd = 0.0758
br_22_s_onpd = round(((3/24)*0.3022) + ((21/24)*0.0829), 4)
br_22_s_offpd = 0.0829
br_22_sp_onpd = round(((3/24)*0.3577) + ((21/24)*0.0853), 4)
br_22_sp_offpd = 0.0853
br_22_w_onpd = round(((3/24)*0.1215) + ((21/24)*0.0758), 4)
br_22_w_offpd = 0.0758
br_23_s = 0.1091
br_23_sp = 0.1157
br_23_w = 0.0803
br_24_s = 0.1089
br_24_sp = 0.1159
br_24_s = 0.0942
br_25_s_onpd = round(((3/24)*0.3022) + ((21/24)*0.0829), 4)
br_25_s_offpd = 0.0829
br_25_sp_onpd = round(((3/24)*0.3577) + ((21/24)*0.0853), 4)
br_25_sp_offpd = 0.0853
br_25_w_onpd = round(((3/24)*0.1215) + ((21/24)*0.0758), 4)
br_25_w_offpd = 0.0758
br_26_s_onpd = round(((7/24)*0.1946) + ((17/24)*0.0727), 4)
br_26_s_offpd = 0.0727
br_26_sp_onpd = round(((7/24)*0.2215) + ((17/24)*0.0730), 4)
br_26_sp_offpd = 0.0730
br_26_w_onpd = round(((8/24)*0.1020) + ((16/24)*0.0711), 4)
br_26_w_offpd = 0.0711
br_29_s_onpd = round(((7/24)*0.1946) + ((11/24)*0.0765) + ((6/24)*0.0616), 4)
br_29_s_offpd = round(((18/24)*0.0765) + ((6/24)*0.0616), 4)
br_29_sp_onpd = round(((7/24)*0.2215) + ((11/24)*0.077) + ((6/24)*0.0619), 4)
br_29_sp_offpd = round(((18/24)*0.077) + ((6/24)*0.0619), 4)
br_29_w_onpd = round(((8/24)*0.102) + ((10/24)*0.0757) + ((6/24)*0.06), 4)
br_29_w_offpd = round(((18/24)*0.0757) + ((6/24)*0.06), 4)
def rate_peak(x):
if x == 'SP_Off-peak21':
return br_21_sp_offpd
elif x == 'SP_Off-peak22':
return br_22_sp_offpd
elif x == 'SP_Off-peak23':
return br_23_sp
elif x == 'SP_Off-peak25':
return br_25_sp_offpd
elif x == 'SP_Off-peak26':
return br_26_sp_offpd
elif x == 'SP_Off-peak29':
return br_29_sp_offpd
elif x == 'SP_On-peak21':
return br_21_sp_onpd
elif x == 'SP_On-peak22':
return br_22_sp_onpd
elif x == 'SP_On-peak23':
return br_23_sp
elif x == 'SP_On-peak25':
return br_25_sp_onpd
elif x == 'SP_On-peak26':
return br_26_sp_onpd
elif x == 'SP_On-peak29':
return br_29_sp_onpd
elif x == 'S_Off-peak21':
return br_21_s_offpd
elif x == 'S_Off-peak22':
return br_22_s_offpd
elif x == 'S_Off-peak23':
return br_23_s
elif x == 'S_Off-peak25':
return br_25_s_offpd
elif x == 'S_Off-peak26':
return br_26_s_offpd
elif x == 'S_Off-peak29':
return br_29_s_offpd
elif x == 'S_On-peak21':
return br_21_s_onpd
elif x == 'S_On-peak22':
return br_22_s_onpd
elif x == 'S_On-peak23':
return br_23_s
elif x == 'S_On-peak25':
return br_25_s_onpd
elif x == 'S_On-peak26':
return br_26_s_onpd
elif x == 'S_On-peak29':
return br_29_s_onpd
elif x == 'W_Off-peak21':
return br_21_w_offpd
elif x == 'W_Off-peak22':
return br_22_w_offpd
elif x == 'W_Off-peak23':
return br_23_w
elif x == 'W_Off-peak25':
return br_25_w_offpd
elif x == 'W_Off-peak26':
return br_26_w_offpd
elif x == 'W_Off-peak29':
return br_29_w_offpd
elif x == 'W_On-peak21':
return br_21_w_onpd
elif x == 'W_On-peak22':
return br_22_w_onpd
elif x == 'W_On-peak23':
return br_23_w
elif x == 'W_On-peak25':
return br_25_w_onpd
elif x == 'W_On-peak26':
return br_26_w_onpd
elif x == 'W_On-peak29':
return br_29_w_onpd
f['elec_cost'] = f['RATE_peak'].apply(rate_peak)
f.drop(columns = ['On-peak D', 'On-peak Day', 'Off-peak Day', 'W_On-peak Day', 'W_Off-peak Day', 'S_On-peak Day', 'S_Off-peak Day', 'SP_On-peak Day', 'SP_Off-peak Day', 'W_On-peak Day_s', 'W_Off-peak Day_s', 'S_On-peak Day_s', 'S_Off-peak Day_s', 'SP_On-peak Day_s', 'SP_Off-peak Day_s', 'c_peak', 'RATE_peak'], inplace = True)
f_s = f[f['summer'] == 1]
f_w = f[f['winter'] == 1]
f_sp = f[f['summer_peak'] == 1]
f_w.to_stata('2015_w_daily.dta')
f_s.to_stata('2015_s_daily.dta')
f_sp.to_stata('2015_sp_daily.dta')
f_s = pd.read_stata('2015_2016_s_daily.dta')
f_w = pd.read_stata('2015_2016_w_daily.dta')
f_sp = pd.read_stata('2015_2016_sp_daily.dta')
f = pd.concat([f_s,f_w,f_sp])
f.sort_values(by=['date_s'], inplace=True)
f.drop(columns = ['index'], inplace = True)
f.to_stata('2015_2016_daily.dta')