[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125079-105":59,"doc-detail-125079-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","enhancing-household-level-operational-decision-making-with-machine-learning","Enhancing Household-Level Operational Decision Making with Machine Learning","","Agent-based modeling offers an effective way to study complex, tightly coupled systems such as electricity markets, yet national-scale ABMs struggle to represent households’ individual decision diversity. Existing approaches that aggregate actors into representative entities and apply a single decision strategy overlook differences in households’ decision-making processes and attributes. This work proposes combining machine learning with ABM by learning and predicting the aggregation of individual actor decisions while maintaining scalability. Uniform forecasting of aggregated demand time series for PVS, HP, and EV is derived from actor-specific optimization models, enabling more nuanced national-level insights.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/enhancing-household-level-operational-decision-making-with-machine-learning/125079/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/enhancing-household-level-operational-decision-making-with-machine-learning/125079.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What challenge does the document address in electricity-market ABMs?","Question",{"text":112,"@type":113},"National-scale agent-based models face scalability limitations when representing households’ individual decision-making. Aggregating actors into representative entities also fails to capture diversity and varying attributes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed method use machine learning?",{"text":117,"@type":113},"It uses machine-learning techniques to learn and predict how individual actor decisions aggregate into market-level demand behavior, while controlling scalability.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the core forecasting mechanism in the approach?",{"text":121,"@type":113},"A uniform forecasting of aggregated demand time series is performed for each actor type—PVS, HP, and EV—where the underlying time series come from actor-specific optimization models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},125079,1785896513,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},962084928904,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","\\# EURO24, Stream: \"Agent-based Models in Management, Economic and Organization Sciences\"  \n\\# Draft 7  \nAuthors: El Ghazi, Aboubakr Achraf; Frey, Ulrich; Sperber, Evelyn  \nTitle: Enhancing Household-Level operational decision making with Machine Learning  \nAgent-based modeling (ABM) presents an effective framework for analyzing complex and highly interconnected systems such as the electricity market. With the ongoing energy transformation, understanding the individual decisions of actors such as households with PV-storage systems (PVS), heat pumps (HP), and electric vehicles (EV) is crucial. However, ABMs for the electricity market on a national level with complex interconnected actors face scalability limitations with regard to their individual decision making. Though, aggregating actors into representative entities and applying a single decision strategy fails to consider the diversity of individual decision-making processes. Moreover, this oversimplified approach neglects the influence of the varying attributes of the individual actors.  \nTo solve this problem, we propose leveraging machine learning (ML) techniques to address these requirements while controlling the scalability challenge. The main idea is to utilize ML methods to learn and predict the aggregation of individual actor decisions. Central to our approach is a uniform forecasting of aggregated demand time series for all actor types, i.e., PVS, HP, and EV. The underlying demand time series result from applying optimization models specific to each actor type. The results show similarly good predictions for PVS, HP, and EV. This method enables a more nuanced understanding the individual impact of decision-making processes of households on a national level.","cbCaifC1xQ2Oautg","https://ap.wps.com/l/cbCaifC1xQ2Oautg","pdf",59772,"English","# Introduction\n## Problem: scalability and decision diversity\n# Proposed Approach\n## ML-based aggregation of actor decisions\n## Uniform demand time-series forecasting for PVS, HP, EV\n# Method Foundations\n## Actor-specific optimization models\n# Results and Implications\n## Prediction quality across actor types\n## National-level understanding of household impacts","[{\"question\":\"What challenge does the document address in electricity-market ABMs?\",\"answer\":\"National-scale agent-based models face scalability limitations when representing households’ individual decision-making. Aggregating actors into representative entities also fails to capture diversity and varying attributes.\"},{\"question\":\"How does the proposed method use machine learning?\",\"answer\":\"It uses machine-learning techniques to learn and predict how individual actor decisions aggregate into market-level demand behavior, while controlling scalability.\"},{\"question\":\"What is the core forecasting mechanism in the approach?\",\"answer\":\"A uniform forecasting of aggregated demand time series is performed for each actor type—PVS, HP, and EV—where the underlying time series come from actor-specific optimization models.\"}]","Enhancing Household-Level Operational Decision Making with Machine Learning | PDF"]