[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122126-en":3,"doc-seo-122126-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122126,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning - A Review","Rising building energy consumption creates environmental and economic pressure, making accurate energy demand prediction a central lever for reduction. Demand prediction (DP) can lower usage, while machine learning (ML) improves performance through data-driven modeling, though results vary across approaches. This review examines and evaluates ML methods for modern building DP, focusing on accuracy and efficiency considerations. It also discusses deployment-relevant aspects of ML within contemporary energy management.","3.0  \n6.2  \nReview  \nEnergy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review  \nSeyed Morteza Moghimi, Thomas Aaron Gulliver and Ilamparithi Thirumai Chelvan  \nSpecial Issue  \nEnergy Systems and Thermal Management for Sustainable Buildings  \nEdited by  \nDr. Haifeng Jiang and Dr. Junxian Pei  \n[https://doi.org/10.3390/en17030555](https://doi.org/10.3390/en17030555)  \n energies   \nReview  \nEnergy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review  \nSeyed Morteza Moghimi *, Thomas Aaron Gulliver  and Ilamparithi Thirumai Chelvan  \nCitation: Moghimi, S.M.; Gulliver, T.A.; Thirumai Chelvan, I. Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review. Energies 2024, 17, 555. [https://](https://)[ ](https://)[doi.org/10.3390/en17030555](doi.org/10.3390/en17030555)  \nAcademic Editors: Umberto Berardi, Junxian Pei and Haifeng Jiang  \nReceived: 7 August 2023  \nRevised: 30 September 2023  \nAccepted: 7 October 2023  \nPublished: 23 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Electrical and Computer Engineering, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada; [agullive@ece.uvic.ca](agullive@ece.uvic.ca) (T.A.G.)  \n* Correspondence: [seyedmortezamoghimi@uvic.ca](seyedmortezamoghimi@uvic.ca)  \nAbstract: Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and ef􀀂ciency. In this paper, these methods are examined and evaluated for modern building (MB) DP.  \nKeywords: demand response; energy 􀀃exibility; green buildings; machine learning; optimization; smart buildings  \n1. Introduction  \nThe design and construction of residential and commercial buildings are among the most energy-intensive activities worldwide. Buildings contribute 20% to 40% of total energy usage [1] . According to the European Union (EU) [2], urban buildings are responsible for 40% of global energy consumption and 33% of greenhouse gas (GHG) emissions. Consequently, governments are motivated to address increasing energy consumption by reducing emissions and improving energy ef􀀂ciency while ensuring the comfort of building residents [3] . To reduce energy consumption, the European Commission (EC) has proposed nearly zero-energy buildings (NZEBs) for 2030 [3] .  \nFigure 1 illustrates the signi􀀂cance of energy reduction in terms of CO 2 emissions and cost based on data from home energy calculators (HECs) [4] . The 􀀂gure gives the results of comprehensive questionnaires administered by a United Kingdom (UK) university. Study participants were randomly assigned one of three versions of the HEC which presented energy consumption in kilowatt hours. Responses were thematically coded by two independent reviewers, leading to 􀀂ve distinct classes: energy-related, cost, environmental, a combination of cost and environmental, and ‘not worth it’, indicating a lack of incentive to reduce energy use, among others.  \nStrategies for demand prediction (DP) [5] are among the solutions recommended by the EC to reduce energy consumption [6,7] . These strategies include price-based demand response (DR), incentive-based DR, time-based DR, automated DR, and capacity-based DR. However, DP has implementation challenges such as operational and technological limitations, as well as data availability and accuracy issues [8] . Machine learning (ML) methods to address these challenges have been proposed [8,9","cbCaiq96Yb01ZrWx","https://ap.wps.com/l/cbCaiq96Yb01ZrWx","pdf",781423,1,21,"English","en",105,"# Introduction\n## Background and policy drivers\n## Demand prediction strategies and challenges\n## Role of machine learning and optimization\n# ML Methods\n## ML process overview\n## Learning paradigms and method categories","[{\"question\":\"Why is energy demand prediction important in modern building energy management?\",\"answer\":\"Increasing building energy use drives environmental and economic concerns. DP helps reduce energy consumption by enabling more informed control of demand.\"},{\"question\":\"What demand response strategies are discussed as part of demand prediction approaches?\",\"answer\":\"The text lists price-based DR, incentive-based DR, time-based DR, automated DR, and capacity-based DR as strategies supporting demand prediction goals.\"},{\"question\":\"How does the paper categorize machine learning methods for building energy prediction?\",\"answer\":\"ML methods are grouped into supervised, unsupervised, and reinforcement learning, with semi-supervised as a hybrid, and further classified into continuous and categorical approaches.\"}]","Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning - A Review | PDF",1785808944,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"energy-management-in-modern-buildings-based-on-demand-prediction-and-machine-learning-a-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/energy-management-in-modern-buildings-based-on-demand-prediction-and-machine-learning-a-review/122126/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",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},"Why is energy demand prediction important in modern building energy management?","Question",{"text":76,"@type":77},"Increasing building energy use drives environmental and economic concerns. DP helps reduce energy consumption by enabling more informed control of demand.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What demand response strategies are discussed as part of demand prediction approaches?",{"text":81,"@type":77},"The text lists price-based DR, incentive-based DR, time-based DR, automated DR, and capacity-based DR as strategies supporting demand prediction goals.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the paper categorize machine learning methods for building energy prediction?",{"text":85,"@type":77},"ML methods are grouped into supervised, unsupervised, and reinforcement learning, with semi-supervised as a hybrid, and further classified into continuous and categorical approaches.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]