[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128282-en":3,"doc-seo-128282-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128282,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning for subnational residential electricity demand forecasting to 2050 under shared socioeconomic pathways - Comparing tree-based, neural and kernel methods","A scenario-based machine learning framework supports long-term, subnational residential electricity demand forecasting by integrating Shared Socioeconomic Pathways (SSPs) with spatially downscaled demographic, economic, and climatic drivers. Using Turkey, the approach projects residential electricity demand to 2050 across 81 provinces and expands training through spatial-temporal multiplicative effects. Random Forest delivers the highest accuracy (R2=0.9359, MAE=0.04 TWh), outperforming neural and kernel models and improving on linear regression. Family households, population, and GDP influence demand more than heating and cooling indicators.","Energy 336 (2025) 138195  \n| Machine learning for subnational residential electricity demand forecasting to 2050 under shared socioeconomic pathways: Comparing tree-based, neural and kernel methods\u003Cbr>Oguzhan Gulaydin ∗, Monjur Mourshed\u003Cbr>School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Long-term energy projections Machine learning\u003Cbr>Shared Socioeconomic Pathways Sub-national energy demand Residential electricity forecasting Random Forest algorithm Turkey (Türkiye) energy planning |  | A scenario-based machine learning framework is presented for long-term, subnational electricity demand forecasting, integrating Shared Socioeconomic Pathways (SSPs) with spatially downscaled demographic, economic, and climatic variables. Using Turkey as a case study, the framework projects residential electricity demand to 2050 across all 81 provinces. The subnational approach enables the use of data-intensive machine learning algorithms by expanding the training dataset through the multiplicative effect of combining spatial and temporal dimensions. Six machine learning models: tree-based (Random Forest, XGBoost), neural networks (Feed-forward Neural Network, Long Short-Term Memory), and kernel-based methods (Support Vector Regression, Gaussian Process Regression), are systematically compared against a traditional linear regression benchmark. Random Forest achieves the highest accuracy (􀁒2 = 0.9359, MAE = 0.04 TWh), outperforming neural and kernel-based models and substantially improving on the linear baseline. Socioeconomic variables, especially family households, population, and GDP, have a greater influence on electricity demand than climatic indicators such as heating and cooling degree days. Turkey’s residential electricity demand is projected to increase by 78% from 65.5 TWh in 2023 to 116.7±2.9 TWh by 2050, with substantial variation across provinces. The spatial variation in demand forecasts highlights the value of subnational modelling for energy planning and the limitations of national-level projections. The use of SSPs enables a consistent and policy-relevant exploration of plausible long-term demand trajectories. By combining subnational resolution, scenario-based inputs, and a structured comparison of algorithm families, the study offers a transferable framework for electricity demand forecasting in regionally diverse or data-scarce contexts, supporting infrastructure planning and decarbonisation strategies. |\n\n1. Introduction  \nGlobal electricity use has steadily increased, driven primarily by population growth, urbanisation, economic development, and technological advancements. The International Energy Agency (IEA) reported a 2.2% increase in global electricity demand in 2023, accelerating to 4.3% in 2024 with projections of nearly 4% annual growth through 2027 [1,2]. The growing demand for energy highlights the need for accurate and reliable long-term electricity demand forecasts for informing energy system planning and policy, and sustainability energy transitions [3]. Projections extending up to 2050 are crucial for developing robust infrastructure and aligning energy systems with sustainability goals, supporting global efforts to transition to resilient energy systems and reduce emissions to net zero by 2050 [4].  \nThe residential sector plays a significant role in the global energy landscape [5]. In 2019, residential electricity consumption accounted  \n∗ Corresponding author.  \nfor approximately 27% of total global usage, ranking it the secondlargest sector after industry [6] (Fig. 1). The factors contributing to rising electricity demand extend beyond global population growth, with the IEA highlighting economic growth, climate conditions, urbanisation, and increasing access to energy-intensive digital technologies as key drivers [2]. Rapid urbanisation, especially in developing countries, has led to more de","cbCairwzxR5nmERO","https://ap.wps.com/l/cbCairwzxR5nmERO","pdf",2972946,3,1,23,"English","en",105,"# Introduction\n## Residential electricity demand drivers\n# Methods and data\n## SSP integration and spatial downscaling\n## Model families compared\n# Results and comparison\n## Accuracy and benchmark performance\n## Influence of socioeconomic vs climatic variables\n# Discussion\n## Subnational value for energy planning\n## Implications for infrastructure and decarbonisation","[{\"question\":\"How does the framework combine SSP scenarios with subnational inputs?\",\"answer\":\"It integrates Shared Socioeconomic Pathways with spatially downscaled demographic, economic, and climatic variables, enabling scenario-based long-term forecasting at the province level.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study systematically compares tree-based (Random Forest, XGBoost), neural networks (Feed-forward Neural Network, LSTM), and kernel-based methods (Support Vector Regression, Gaussian Process Regression) against a linear regression benchmark.\"},{\"question\":\"What factors influence residential electricity demand more strongly than climate indicators?\",\"answer\":\"Socioeconomic variables—especially family households, population, and GDP—show a greater influence than climatic indicators such as heating and cooling degree days.\"}]","Machine learning for subnational residential electricity demand forecasting to 2050 under shared socioeconomic pathways - Comparing tree-based, neural and kernel methods | PDF",1785946535,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-for-subnational-residential-electricity-demand-forecasting-to-2050-under-shared-socioeconomic-pathways-comparing-tree-based-neural-and-kernel-methods","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-for-subnational-residential-electricity-demand-forecasting-to-2050-under-shared-socioeconomic-pathways-comparing-tree-based-neural-and-kernel-methods/128282/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the framework combine SSP scenarios with subnational inputs?","Question",{"text":76,"@type":77},"It integrates Shared Socioeconomic Pathways with spatially downscaled demographic, economic, and climatic variables, enabling scenario-based long-term forecasting at the province level.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared in the study?",{"text":81,"@type":77},"The study systematically compares tree-based (Random Forest, XGBoost), neural networks (Feed-forward Neural Network, LSTM), and kernel-based methods (Support Vector Regression, Gaussian Process Regression) against a linear regression benchmark.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors influence residential electricity demand more strongly than climate indicators?",{"text":85,"@type":77},"Socioeconomic variables—especially family households, population, and GDP—show a greater influence than climatic indicators such as heating and cooling degree days.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]