Leticia Tomas Fillol , Nicolò Stevanato , Antti Pinomaa , Riccardo Mereu , Samuli Honkapuro
{"title":"Improving electricity demand growth estimation in rural mini-grids through data-driven appliance diffusion modeling","authors":"Leticia Tomas Fillol , Nicolò Stevanato , Antti Pinomaa , Riccardo Mereu , Samuli Honkapuro","doi":"10.1016/j.esd.2025.101826","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate load modeling is crucial for designing reliable and cost-effective mini-grids in rural, under-served communities accessing electricity for the first time. Current models often fail to capture the evolving energy demands associated with changes in appliance ownership and socio-economic growth. The study introduces a bottom-up, adaptable model that forecasts a progressive appliance adoption and customer base in order to improve long-term electricity demand estimations. This study employs real-world appliance adoption trends from rural Kenya as a case study and uses logistic diffusion and optimization techniques to model appliance diffusion. The findings highlight significant variability in appliance adoption rates already during the first years of electricity access between different household groups, identified through clustering algorithms. This variability underscores the need for dynamic modeling over traditional static categorization of end users to more accurately reflect evolving consumer energy consumption profiles. The proposed model serves as a tool to enhance multiyear load profile generation and support microgrid design in similar settings.</div></div>","PeriodicalId":49209,"journal":{"name":"Energy for Sustainable Development","volume":"89 ","pages":"Article 101826"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy for Sustainable Development","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0973082625001760","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate load modeling is crucial for designing reliable and cost-effective mini-grids in rural, under-served communities accessing electricity for the first time. Current models often fail to capture the evolving energy demands associated with changes in appliance ownership and socio-economic growth. The study introduces a bottom-up, adaptable model that forecasts a progressive appliance adoption and customer base in order to improve long-term electricity demand estimations. This study employs real-world appliance adoption trends from rural Kenya as a case study and uses logistic diffusion and optimization techniques to model appliance diffusion. The findings highlight significant variability in appliance adoption rates already during the first years of electricity access between different household groups, identified through clustering algorithms. This variability underscores the need for dynamic modeling over traditional static categorization of end users to more accurately reflect evolving consumer energy consumption profiles. The proposed model serves as a tool to enhance multiyear load profile generation and support microgrid design in similar settings.
期刊介绍:
Published on behalf of the International Energy Initiative, Energy for Sustainable Development is the journal for decision makers, managers, consultants, policy makers, planners and researchers in both government and non-government organizations. It publishes original research and reviews about energy in developing countries, sustainable development, energy resources, technologies, policies and interactions.