Neve Fields , Ariane Millot , Martin Mutembei , Anne Nganga , Pietro Lubello , Leonhard Hofbauer , Mark Howells , Ed Brown
{"title":"需求启动数据包:肯尼亚所有47个县的选定的社会经济和技术能源系统需求建模数据","authors":"Neve Fields , Ariane Millot , Martin Mutembei , Anne Nganga , Pietro Lubello , Leonhard Hofbauer , Mark Howells , Ed Brown","doi":"10.1016/j.dib.2025.111556","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111556"},"PeriodicalIF":1.0000,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demand starter data kit: Selected socio-economic and technical energy system demand modelling data for all 47 counties in Kenya\",\"authors\":\"Neve Fields , Ariane Millot , Martin Mutembei , Anne Nganga , Pietro Lubello , Leonhard Hofbauer , Mark Howells , Ed Brown\",\"doi\":\"10.1016/j.dib.2025.111556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"60 \",\"pages\":\"Article 111556\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352340925002884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340925002884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.