[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125792-en":3,"doc-seo-125792-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125792,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Applications for Smart Building Energy Utilization - A Survey","The United Nations launched sustainable development goals in 2015, including targets for sustainable energy, while household energy use in Europe, North America and Asia accounts for 20–30% of total consumption and global demand continues to rise. To meet growing demand and enable efficient energy use in buildings, machine learning and related AI methods require structured application development. This paper provides a systematic literature review and a taxonomy for smart building energy utilization applications, analyzing key solution areas and discussing open issues and future directions.","Archives of Computational Methods in Engineering [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1831-023-10054-7  \nMachine Learning Applications for Smart Building Energy Utilization: A Survey  \nMatti Huotari1,2 · Avleen Malhi1,3 · Kary Främling1,4  \nReceived: 3 August 2022 / Accepted: 17 December 2023 © The Author(s) 2024  \nAbstract  \nThe United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization,(b) a novel taxonomy for the applications,(c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally,(d) a discussion on open issues and developments in the field.  \n1 Introduction  \nOverall, households account for 20–30% of energy consumption in Europe, North America and Asia. Heating and cooling, lighting, and electric appliances are the three major contributors of this consumption [1, 2]. Moreover, the recent regulation in the EU and China has required buildings to utilize less energy and, at the same time, utilize more renewable energy [3 , 4] . To promote efficient and comfortable energy consumption in smart buildings, there is a need to develop and deploy machine learning (ML) applications, neural network (NN) applications and other AI applications coupled with systematic data. However, despite the potential significance of AI in this area, few surveys or reviews have  \n* Matti Huotari[matti.huotari@aalto.fi](matti.huotari@aalto.fi)  \n* Avleen Malhi  \n* Kary Främling  \n1 School of Science, Aalto University, Espoo, Finland  \n2 Metropolia University of Applied Sciences, Helsinki, Finland  \n3 Warwick University, Coventry, UK  \n4 Umeå University, Umeå, Sweden  \nsystematically categorized machine learning applications for energy utilization in smart buildings.  \nThe purpose of this study is to review the application segments of the smart energy of buildings since 2009 using a mapping study to scope the topic, and, then, to concentrate on the review of the applications and techniques within that scope. We present our method in Sect. 2. We present our mapping study results and answer the related research questions in Sect. 3. In Sect. 4 we present our literature review results and in Sect. 5 answer the related research questions. We present the open issues and future work in Sect. 6 and our conclusions in Sect. 7.  \n2 Method  \nThis study has been undertaken as a mixed study starting with a mapping study to scope the study topic and continuing with a related systematic literature review [5 , 6] . The first goal of this study is to assess the status of application segments for the smart energy of buildings. This part of the study can be categorized as a mapping study. Then, the second goal of this study is to review the applications and the machine learning techniques identified through the mapping study. This part of the study can be categorized as a  \nsystematic literature review. The steps of the mapping and  \nsystematic literature review are presented below.  \n2.1 Research Questions  \nThe research questions addressed by this stu","cbCaiau6gzS82xdk","https://ap.wps.com/l/cbCaiau6gzS82xdk","pdf",1467011,1,20,"English","en",105,"# Introduction\n## Research Questions\n# Method\n## Research Questions\n# Mapping Study and Systematic Review Results\n## Literature Review Results\n# Open Issues and Future Work\n# Conclusions","[{\"question\":\"Why is there a need for machine learning applications in smart building energy utilization?\",\"answer\":\"Households consume 20–30% of energy and energy demand has increased, while regulations push buildings toward lower energy use and higher renewable integration. Machine learning can support efficient and comfortable energy consumption through data-driven applications and systematic methods.\"},{\"question\":\"What are the main contributions of the paper?\",\"answer\":\"The paper (a) reviews application segments and machine learning methods, (b) proposes a novel taxonomy, (c) analyzes solutions and techniques across areas such as grid, energy management/control, maintenance/security, and personalization, and (d) discusses open issues and developments.\"},{\"question\":\"How does the study conduct the review?\",\"answer\":\"It uses a mixed approach: a mapping study to scope application segments since 2009, followed by a systematic literature review focused on applications and techniques within that scope. The method is tied to specific research questions (RQ1–RQ4).\"}]","Machine Learning Applications for Smart Building Energy Utilization - A Survey | PDF",1785901222,50,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-applications-for-smart-building-energy-utilization-a-survey","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-applications-for-smart-building-energy-utilization-a-survey/125792/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is there a need for machine learning applications in smart building energy utilization?","Question",{"text":75,"@type":76},"Households consume 20–30% of energy and energy demand has increased, while regulations push buildings toward lower energy use and higher renewable integration. Machine learning can support efficient and comfortable energy consumption through data-driven applications and systematic methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main contributions of the paper?",{"text":80,"@type":76},"The paper (a) reviews application segments and machine learning methods, (b) proposes a novel taxonomy, (c) analyzes solutions and techniques across areas such as grid, energy management/control, maintenance/security, and personalization, and (d) discusses open issues and developments.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study conduct the review?",{"text":84,"@type":76},"It uses a mixed approach: a mapping study to scope application segments since 2009, followed by a systematic literature review focused on applications and techniques within that scope. 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