Energy Research Centre 2020-04-29 NADA Version 4 Domestic Electrical Load Survey - Key Variables 1994-2014 DELSKV 1994-2014 zaf-erc-delskv-1994-2014-v1 Wiebke Toussaint

Marcus Dekenah

 NADA South African National Energy Development Initiative CESAR Programme DataFirst Support Site Household Survey [hh] Toussaint, Wiebke. Domestic Electrical Load Survey - Key Variables 1994-2014 [dataset]. version 1. Johannesburg: SANEDI [funders]. Cape Town: UCT Energy Research Centre [producers], 2014. Cape Town: DataFirst [distributor], 2019. DOI: https://doi.org/10.25828/mf8s-hh79 v1: licensed data, available under conditions Version 1 electrical household load data domestic electrification electricity consumption energy rural residential appliance This dataset is a harmonisation of the Domestic Electrical Load Survey (DELS) 1994-2014 dataset. The DELS 1994-2014 questionnaires were changed in 2000. Subsequently survey questions vary between 1994-1999 and 2000-2014. This makes data processing complex, as survey responses first need to be associated with their year of collection and corresponding questionnaire before they can be correctly interpreted. This Key Variables dataset is a user-friendly version of the original dataset. It contains household responses to the most important survey questions, as well as geographic and linking information that allows for the households to be matched to their respective electricity metering data. This dataset and similar custom datasets can be produced from the DELS 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). South Africa The study had national coverage. Settlement/suburb and municipality Households The dataset covers South African households in the DELS 1994-2014 dataset. These are electrified households that received electricity either directly from Eskom or from their local municipality. Administrative records The scope of the South African Domestic Electrical Load Survey includes: HOUSEHOLD: Household characteristics, occupant demographics of gender and age, employment, income, number of household occupants with income DWELLING: Dwelling characteristics, wall material, roof material, floor area, water source APPLIANCES: stove, hotplate, kettle, heater, iron, geyser, washing machine, TV, fridge, freezer, microwave BEHAVIOUR: frequency of appliance usage ELECTRICITY: time since electrification, circuit breaker The dataset includes all households for which survey responses have been captured in the DELS1994-2014 dataset. Face-to-face [f2f] Data is provided as is and no weightings have been applied. This dataset has been constructed from the DELS 1994-2014 dataset using the data processing functions in the delprocess python package (www.github.com/wiebket/delprocess: release v1.0). The delprocess python package takes the complexities of the original DELS 1994-2014 dataset into account and makes use of 'spec files' to specify the processing steps that must be performed. To retrieve data for all survey years, two separate spec files are required to process survey response from 1994-1999 and 2000-2014. The spec files used to produce this dataset are included in the program files and can be used as templates for new custom datasets. Full instructions on how to use them to process the data are in the README file contained in the delprocess package. SPEC FILES specify the following processing steps: 1. List of search terms for which survey questions will be searched, and variables returned 2. Transformations (addition, subtraction, multiplication) of variables retrieved from search output 3. Bin intervals for variables (requires numeric data) 4. Lables for bins (requires binned data) 5. Details of bin segments 6. Replacement (encoding) of coded variable values 7. Higher level geography detail In particular, the DELSKV 1994-2014 dataset has been produced by specifying the following processing steps: TRANSFORMATIONS * monthly_income from 1994 - 1999 is the variable returned by the 'income' search term * monthly_income from 2000 - 2014 is calculated as the sum of the variables returned by the 'earn per month', 'money from small business' and 'external' search terms * Appliance numbers from 1994 - 1999 is the count of appliances (no data was collected on broken appliances) * Appliance numbers from 2000-2014 is the count of appliances [minus] the count of broken appliances (except for TV which included no information on broken appliances) * A new total_adults variable was created by summing the number of all occupants (male and female) over 16 years old * A new total_children variable was created by summing the number of all occupants (male and female) under 16 years old * A new total_pensioners variable was created by summing the number of pensioners (male and female) over 16 years old * A new total_unemployed variable was created by summing the number of unemployed occupants (male and female) over 16 years old * A new total_part_time variable was created by summing the number of part time employed occupants (male and female) over 16 years old * roof_material and wall_material values for 1994-1999 were augmented by 1 * water_access was transformed for 1994-1999 to be 4 [minus] the 'watersource' value REPLACEMENTS * Appliance usage values have been replaced with: 0=never 1=monthly 2=weekly 3=daily * water_access values have been replaced with: 1=nearby river/dam/borehole 2=block/street taps 3=tap in yard 4=tap inside house * roof_material and wall_material values have been replaced with: 1=IBR/Corr.Iron/Zinc 2=Thatch/Grass 3=Wood/Masonite board 4=Brick 5=Block 6=Plaster 7=Concrete 8=Tiles 9=Plastic 10=Asbestos 11=Daub/Mud/Clay OTHER NOTES Appliance usage information was only collected after 2000. No binning was done to segment survey responses for this dataset. * The 2000-2014 survey questions contain no variable for 'number of females: 50+', which goes against the pattern of other occupant age categories. * Spacing in the original questions is irregular and can cause challenges when specifying transformations (eg. 'number of males: 16-24' and 'number of males: 25 - 34', 'part time' and 'parttime'). * Spelling mistakes in the original questions can cause challenges when specifying transformations (eg. 'head emploed part time'). MISSING VALUES Missing values have not been replaced and are represented as blanks except for imputed columns (total_adults, total_children, ...) and appliances after 2000, where missing values have been replaced with a 0. DataFirst Toussaint, Wiebke. Domestic Electrical Load Survey - Key Variables 1994-2014 [dataset]. version 1. Johannesburg: SANEDI [funders]. Cape Town: UCT Energy Research Centre [producers], 2014. Cape Town: DataFirst [distributor], 2019. DOI: https://doi.org/10.25828/mf8s-hh79 Licensed use files, available for non-commercial use only DELSKV 0 79 id appliance dist_base behaviour number_of_females appliance_usage appliance ownership dwelling_characteristics location income_generation Monthly household income and income generation potential. Includes employed, part time employed and pensioners details. occupants number_of_males totals unemployed part_time_employed pensioners ProfileID ProfileID ProfileID ProfileID ProfileID AnswerID AnswerID AnswerID AnswerID AnswerID Unit of measurement Unit of measurement Unit of measurement Unit of measurement Unit of measurement Survey Survey Survey Survey Survey QuestionaireID QuestionaireID QuestionaireID QuestionaireID QuestionaireID Year Year Year Year Year LocName LocName LocName LocName LocName Municipality Municipality Municipality Municipality Municipality cb_size cb_size cb_size cb_size cb_size females16to24 females16to24 females16to24 females16to24 females16to24 females25to34 females25to34 females25to34 females25to34 females25to34 females35to49 females35to49 females35to49 females35to49 females35to49 femalesolder50 femalesolder50 femalesolder50 femalesolder50 femalesolder50 femalesyounger16 femalesyounger16 femalesyounger16 femalesyounger16 femalesyounger16 floor_area floor_area floor_area floor_area floor_area head emploed part time head emploed part time head emploed part time head emploed part time head emploed part time head on pension head on pension head on pension head on pension head on pension head unemployed head unemployed head unemployed head unemployed head unemployed headparttime headparttime headparttime headparttime headparttime headunemployed headunemployed headunemployed headunemployed headunemployed males16to24 males16to24 males16to24 males16to24 males16to24 males25to34 males25to34 males25to34 males25to34 males25to34 males35to49 males35to49 males35to49 males35to49 males35to49 malesolder50 malesolder50 malesolder50 malesolder50 malesolder50 malesyounger16 malesyounger16 malesyounger16 malesyounger16 malesyounger16 monthly_income monthly_income monthly_income monthly_income monthly_income number of females: 16-24 number of females: 16-24 number of females: 16-24 number of females: 16-24 number of females: 16-24 number of females: 25-34 number of females: 25-34 number of females: 25-34 number of females: 25-34 number of females: 25-34 number of females: 35 - 49 number of females: 35 - 49 number of females: 35 - 49 number of females: 35 - 49 number of females: 35 - 49 number of females: <16 number of females: <16 number of females: <16 number of females: <16 number of females: <16 number of males: 16-24 number of males: 16-24 number of males: 16-24 number of males: 16-24 number of males: 16-24 number of males: 25 - 34 number of males: 25 - 34 number of males: 25 - 34 number of males: 25 - 34 number of males: 25 - 34 number of males: 35-49 number of males: 35-49 number of males: 35-49 number of males: 35-49 number of males: 35-49 number of males: 50+ number of males: 50+ number of males: 50+ number of males: 50+ number of males: 50+ number of males: <16 number of males: <16 number of males: <16 number of males: <16 number of males: <16 older16parttime older16parttime older16parttime older16parttime older16parttime older16unemployed older16unemployed older16unemployed older16unemployed older16unemployed persions younger than 16 year : employed parttime persions younger than 16 year : employed parttime persions younger than 16 year : employed parttime persions younger than 16 year : employed parttime persions younger than 16 year : employed parttime persons older than 16 years: employed parttime persons older than 16 years: employed parttime persons older than 16 years: employed parttime persons older than 16 years: employed parttime persons older than 16 years: employed parttime persons older than 16 years: employed unemployed persons older than 16 years: employed unemployed persons older than 16 years: employed unemployed persons older than 16 years: employed unemployed persons older than 16 years: employed unemployed persons older than 16 years: pension persons older than 16 years: pension persons older than 16 years: pension persons older than 16 years: pension persons older than 16 years: pension persons younger than 16 years: pension persons younger than 16 years: pension persons younger than 16 years: pension persons younger than 16 years: pension persons younger than 16 years: pension persons younger than 16 years: unemployed persons younger than 16 years: unemployed persons younger than 16 years: unemployed persons younger than 16 years: unemployed persons younger than 16 years: unemployed roof_material roof_material roof_material roof_material roof_material spouse employed part time spouse employed part time spouse employed part time spouse employed part time spouse employed part time spouse on pension spouse on pension spouse on pension spouse on pension spouse on pension spouse unemployed spouse unemployed spouse unemployed spouse unemployed spouse unemployed spouseparttime spouseparttime spouseparttime spouseparttime spouseparttime spouseunemployed spouseunemployed spouseunemployed spouseunemployed spouseunemployed total_adults total_adults total_adults total_adults total_adults total_children total_children total_children total_children total_children total_part_time total_part_time total_part_time total_part_time total_part_time total_pensioners total_pensioners total_pensioners total_pensioners total_pensioners total_unemployed total_unemployed total_unemployed total_unemployed total_unemployed wall_material wall_material wall_material wall_material wall_material water_access water_access water_access water_access water_access years_electrified years_electrified years_electrified years_electrified years_electrified younger16parttime younger16parttime younger16parttime younger16parttime younger16parttime younger16unemployed younger16unemployed younger16unemployed younger16unemployed younger16unemployed 3_plate 3_plate 3_plate 3_plate 3_plate 4_plate 4_plate 4_plate 4_plate 4_plate fridge_freezer fridge_freezer fridge_freezer fridge_freezer fridge_freezer geyser geyser geyser geyser geyser heater heater heater heater heater hotplate hotplate hotplate hotplate hotplate iron iron iron iron iron kettle kettle kettle kettle kettle microwave microwave microwave microwave microwave tv tv tv tv tv washing_machine washing_machine washing_machine washing_machine washing_machine 3_plate_use 3_plate_use 3_plate_use 3_plate_use 3_plate_use 4_plate_use 4_plate_use 4_plate_use 4_plate_use 4_plate_use geyser_use geyser_use geyser_use geyser_use geyser_use heater_use heater_use heater_use heater_use heater_use hotplate_use hotplate_use hotplate_use hotplate_use hotplate_use iron_use iron_use iron_use iron_use iron_use kettle_use kettle_use kettle_use kettle_use kettle_use microwave_use microwave_use microwave_use microwave_use microwave_use washing_machine_use washing_machine_use washing_machine_use washing_machine_use washing_machine_use