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:
The following geographic locations were selected to obtain the hourly profile and summary datasets for each class:
DPET does not provide data for urban townhouse (lsm9-10) and urban estate (lsm10+) consumers (above R20k monthly income).
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#Import statements
import benchmark.bm0 as bm0
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[print(c) for c in bm0.expertDemandSummary()['class'].unique()]
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bm0.expertDemandSummary().head()
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In [2]:
bm0.plotBmDemandSummary('township')
bm0.plot15YearBmDemandSummary()
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bm0.bmHourlyProfiles()[100:110]
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bm0.plotBmHourlyProfiles(customer_class = 'informal_settlement', year_list = [1, 7, 15], daytype = 'Weekday')
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bm0.plotBmHourlyProfiles(customer_class = 'urban_res_7', year_list = [1, 7, 15], daytype = 'Weekday')
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bm0.plotBmHourlyProfiles(customer_class = 'urban_res_8-9', year_list = [1, 3, 7], daytype = 'Weekday')