Demand starter data kit: Selected socio-economic and technical energy system demand modelling data for all 47 counties in Kenya

The need for data-driven models to inform energy planning and policy making is increasingly important as Kenya looks to transform its energy system to be clean, efficient, diverse and secure. Modelling softwares can be used by policy makers to assess the impacts of different scenarios on energy systems to support planning and decision making. Demand forms an integral foundation of energy planning and insights into possible projections can aid in policy creation, yet access to data is often a barrier to utilising energy demand modelling to support such decision making. Despite the official launch of the energy governance devolution process within Kenya, through the Kenya Energy Act (2019), progress towards county energy planning and developing modelling data and tools to reflect this remains limited and inaccessible. Therefore, this article provides data that can be used to create a simple whole energy system demand model for the individual counties in Kenya, acting as a starting point for teaching, capacity building efforts, and for further data collection, model development and scenario analysis to produce county resolution demand projections. The data was collected from websites, annual reports, and databases of international and national organisations alongside existing modelling databases and academic articles and can be easily updated based on the latest available local data and information. As a demonstration, these data were used to calibrate a demand model for Kilifi County using the Model for the Analysis of Energy Demand (MAED) for a baseline scenario from 2019 to 2070. The assumptions used and results gained are illustrated in the appendix of the article as a demonstration of what can be achieved through application of this dataset.

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Publication Details

Journal
Data in Brief
Published
2025-04-17
DOI
https://doi.org/10.1016/j.dib.2025.111556
Citations
2
Primary Topic
Energy and Environment Impacts
Type
article
Field-Weighted Citation Impact
1.01
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article

Demand starter data kit: Selected socio-economic and technical energy system demand modelling data for all 47 counties in Kenya

Pietro Lubello, Ariane Millot, Neve Fields, Ed Brown et al.
2 citations
Data in Brief
Energy and Environment Impacts
1.01
article

Demand starter data kit: Selected socio-economic and technical energy system demand modelling data for all 47 counties in Kenya

Pietro Lubello, Ariane Millot, Neve Fields, Ed Brown, Leonhard Hofbauer, Martin Mutembei, Mark Howells, Anne Nganga
article en
2 citations

Abstract

The need for data-driven models to inform energy planning and policy making is increasingly important as Kenya looks to transform its energy system to be clean, efficient, diverse and secure. Modelling softwares can be used by policy makers to assess the impacts of different scenarios on energy systems to support planning and decision making. Demand forms an integral foundation of energy planning and insights into possible projections can aid in policy creation, yet access to data is often a barrier to utilising energy demand modelling to support such decision making. Despite the official launch of the energy governance devolution process within Kenya, through the Kenya Energy Act (2019), progress towards county energy planning and developing modelling data and tools to reflect this remains limited and inaccessible. Therefore, this article provides data that can be used to create a simple whole energy system demand model for the individual counties in Kenya, acting as a starting point for teaching, capacity building efforts, and for further data collection, model development and scenario analysis to produce county resolution demand projections. The data was collected from websites, annual reports, and databases of international and national organisations alongside existing modelling databases and academic articles and can be easily updated based on the latest available local data and information. As a demonstration, these data were used to calibrate a demand model for Kilifi County using the Model for the Analysis of Energy Demand (MAED) for a baseline scenario from 2019 to 2070. The assumptions used and results gained are illustrated in the appendix of the article as a demonstration of what can be achieved through application of this dataset.

Data in BriefVol. 60
Energy Institute (GB), Loughborough University (GB), Strathmore University (KE), University College London (GB), Imperial College London (GB)
Affordable and clean energy
Openalex Percentile: Top 28%
Energy and Environment Impacts
1.01
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