Domestic Load Research Programme Expert Model Extracted from DPET

This notebook requires access to a data directory with data extracted from the Demand Planning & Electrification Tool (DPET). The data files must be in the directory on the path /data/dpet/ .

This expert model has been extracted from DPET according to Table 6 set out in the Geo-based Load Forecast Standard (p.47) using software released with 2013 data and the following user input settings:

2011 income values
all default settings

The following geographic locations were selected to obtain the hourly profile and summary datasets for each class:

rural: Westrand District Municipality (R1000 mean income)
village: Westrand District Municipality (R1000 mean income)
informal settlement: City of Johannesburg Metro (R2000 mean income)
township: City of Johannesburg Metro (R5500 mean income)
urban residential (lsm7): City of Johannesburg Metro (R10 000 mean income)
urban residential (lsm8-9): City of Johannesburg Metro (R18 000 mean income)
urban townhouse (lsm7-8): City of Johannesburg Metro (R15 500 mean income)

DPET does not provide data for urban townhouse (lsm9-10) and urban estate (lsm10+) consumers (above R20k monthly income).



In [1]:

    
#Import statements
import benchmark.bm0 as bm0

Interrogate Summary Class Data



In [5]:

    
[print(c) for c in bm0.expertDemandSummary()['class'].unique()]









    



informal_settlement
township
urban_res_7
urban_townhouse_7-8
urban_res_8-9
rural






    Out[5]:





[None, None, None, None, None, None]



In [6]:

    
bm0.expertDemandSummary().head()









    Out[6]:







  
    
      
      YearsElectrified
      Energy [kWh]
      ADMD [kVA]
      Alpha
      Beta
      Circuit Breaker
      class
    
  
  
    
      0
      1
      151.123757
      0.659224
      0.216751
      1.295713
      20
      informal_settlement
    
    
      1
      2
      154.539101
      0.672016
      0.218622
      1.277860
      20
      informal_settlement
    
    
      2
      3
      158.168829
      0.685610
      0.220576
      1.259346
      20
      informal_settlement
    
    
      3
      4
      161.819533
      0.699283
      0.222505
      1.241173
      20
      informal_settlement
    
    
      4
      5
      165.297803
      0.712310
      0.224310
      1.224252
      20
      informal_settlement



In [2]:

    
bm0.plotBmDemandSummary('township')
bm0.plot15YearBmDemandSummary()

Interrogate Hourly Profile Data



In [8]:

    
bm0.bmHourlyProfiles()[100:110]









    Out[8]:







  
    
      
      YearsElectrified
      Energy [kWh]
      month
      daytype
      hour
      Mean [kVA]
      Stdev [kVA]
      class
    
  
  
    
      100
      1
      79.663
      2
      Saturday
      4
      0.066
      0.029
      rural
    
    
      101
      1
      79.663
      2
      Saturday
      5
      0.085
      0.040
      rural
    
    
      102
      1
      79.663
      2
      Saturday
      6
      0.113
      0.048
      rural
    
    
      103
      1
      79.663
      2
      Saturday
      7
      0.124
      0.046
      rural
    
    
      104
      1
      79.663
      2
      Saturday
      8
      0.120
      0.042
      rural
    
    
      105
      1
      79.663
      2
      Saturday
      9
      0.114
      0.044
      rural
    
    
      106
      1
      79.663
      2
      Saturday
      10
      0.109
      0.044
      rural
    
    
      107
      1
      79.663
      2
      Saturday
      11
      0.106
      0.047
      rural
    
    
      108
      1
      79.663
      2
      Saturday
      12
      0.105
      0.045
      rural
    
    
      109
      1
      79.663
      2
      Saturday
      13
      0.105
      0.043
      rural



In [4]:

    
bm0.plotBmHourlyProfiles(customer_class = 'informal_settlement', year_list = [1, 7, 15], daytype = 'Weekday')



In [5]:

    
bm0.plotBmHourlyProfiles(customer_class = 'urban_res_7', year_list = [1, 7, 15], daytype = 'Weekday')



In [9]:

    
bm0.plotBmHourlyProfiles(customer_class = 'urban_res_8-9', year_list = [1, 3, 7], daytype = 'Weekday')

	YearsElectrified	Energy [kWh]	ADMD [kVA]	Alpha	Beta	Circuit Breaker	class
0	1	151.123757	0.659224	0.216751	1.295713	20	informal_settlement
1	2	154.539101	0.672016	0.218622	1.277860	20	informal_settlement
2	3	158.168829	0.685610	0.220576	1.259346	20	informal_settlement
3	4	161.819533	0.699283	0.222505	1.241173	20	informal_settlement
4	5	165.297803	0.712310	0.224310	1.224252	20	informal_settlement

	YearsElectrified	Energy [kWh]	month	daytype	hour	Mean [kVA]	Stdev [kVA]	class
100	1	79.663	2	Saturday	4	0.066	0.029	rural
101	1	79.663	2	Saturday	5	0.085	0.040	rural
102	1	79.663	2	Saturday	6	0.113	0.048	rural
103	1	79.663	2	Saturday	7	0.124	0.046	rural
104	1	79.663	2	Saturday	8	0.120	0.042	rural
105	1	79.663	2	Saturday	9	0.114	0.044	rural
106	1	79.663	2	Saturday	10	0.109	0.044	rural
107	1	79.663	2	Saturday	11	0.106	0.047	rural
108	1	79.663	2	Saturday	12	0.105	0.045	rural
109	1	79.663	2	Saturday	13	0.105	0.043	rural