Domestic Electrical Load Metering, Hourly Data 1994-2014
Hourly Data 1994-2014
Service Provision Assessment [hh/spa]
This data is an aggregated subset of the 5-minute interval electricity metering data available in the Domestic Electrical Load Metering Data (DELM) 1994-2014 available in DataFirst's secure centre. The large volume and high metering cadence of the DELM 1994-2014 data is unwieldy to access and process. Many applications that do not require the granularity of the DELM 1994-2014 data will be able to extract value more effectively and conveniently from aggregate values. This dataset contains all current (Amps) observations aggregated to hourly values. It can be easily merged with the Domestic Electrical Load Survey Key Variables 1994-2014 data to link socio-demographic varibles with household consumption data. This dataset and similar custom datasets can be produced from the DELM 1994-2014 dataset with the python package delprocess. The data processing section includes a description of how this dataset was created. The development of the tools to create this dataset was funded by the South African National Energy Development Initiative (SANEDI).
Kind of Data
Unit of Analysis
v1: Edited, anonymised data for licensed distribution
The scope of the hourly South African Domestic Electrical Load Metering data includes:
CURRENT: Amperes (A) aggregated over a 60 minute interval defined to start daily at 00:00:00 - 00:59:59.
The study had national coverage.
The DELMH 1994-2014 dataset does not contain geographic information. When combined with the DELSKV 1994-2014 dataset, the lowest unit of geographic aggregation is settlement/suburb. Municipal-level data is also available in the DELSKV data which can be merged with the DELMH data.
The metering study covers electrified households that received electricity either directly from Eskom or from their local municipality. Particular attention was devoted to rural and low income households, as well as surveying households electrified over a range of years, thus having had access to electricity from recent times to several decades.
Producers and sponsors
University of Cape Town
South African National Energy Development Initiative
[Expert knowledge for] understanding database design and conveying details around data collection
Dates of Data Collection
Data Collection Mode
This data has been produced by aggregating all current (Amps) metering data from the DELMS 1994-2014 dataset using the reduceRawProfiles function from the delprocess python package (https://github.com/wiebket/delprocess: release v1.0). Full instructions on how to use delprocess to aggregate metering data are in the README file contained in the package.
The 'Valid' indicator of readings was converted to 1 (valid) and 0 (invalid). Missing 'Valid' indicators were filled with 0 values.
Missing readings were treated as per pandas.dataframe.mean default: skipna = True; i.e. missing values are excluded when computing results.
DATA AGGREGATION (OBSERVATIONS)
The following processing steps were performed to produce the aggregate dataset:
0. 'Datefield' values were converted to integer values, rounded to 9 positions left of the decimal, and converted to a numpy datetime64 object with nano-second units. This was done to coerce the data to consistent time intervals.
1. readings grouped by RecorderID and ProfileID
2. grouped data resampled to hourly values ('Datefield' column converted to 'H' offset)
3. mean meter reading value and 'Valid' indicator calculated over resampled intervals
4. rows with all missing values removed
5. aggregated 'Valid' indicator set to 0 unless it was 1 (i.e. if one reading was marked as invalid, the mean 'Valid' indicator would be less than 1 and the aggregate 'Valid' indicator was set to 0, thus marking the aggregated validity as invalid)
DATA AGGREGATION (STUDY CYCLES)
Data was aggregated per year, across temporally overlapping study cycles.
University of Cape Town
Licensed use files, available with restrictions
Toussaint, Wiebke. Domestic Electrical Load Metering, Hourly Data 1994-2014 [dataset]. Version 1. Johannesburg: SANEDI [funders]. Cape Town: Energy Research Centre, UCT [producers], 2014. Cape Town: DataFirst [distributor], 2019. DOI: https://doi.org/10.25828/56nh-fw77