[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116834-en":3,"doc-seo-116834-105":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},116834,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning Occupancy Prediction Models - A Case Study","Energy reduction in the building sector is essential to curb associated carbon emissions, which represent 39% of total emissions globally. Building occupancy is a key driver of building services operation and energy use, so time-resolved occupancy information can strengthen energy and environmental performance. This study evaluates Hidden Markov Model and Artificial Neural Network models for predicting occupancy numbers in a high-density higher education building in England using high-resolution five-minute interval data across workdays, weekends, holidays, and vacations.","Machine learning occupancy prediction models - a case study  \nConference or Workshop Item  \nPublished Version  \nAlfalah, B., Shahrestani, M. ORCID: [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)[0002-8741-0912 and Shao](0002-8741-0912 and Shao) , L. (2023) Machine learning occupancy prediction models - a case study. In: ASHRAE winter conference 2023, 4-8 Feb 2023, Atlanta. (129 (1)) Available at [https://centaur. reading.ac. uk/107547/](https://centaur. reading.ac. uk/107547/)  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nPublisher statement: ©2023. ASHRAE ([www.ashrae.org](www.ashrae.org) ). Published in 2023 ASHRAE Transactions, Volume 129, Part 1. This article may not be copied and/or distributed electronically or in paper form without permission of ASHRAE.  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nMachine Learning Occupancy Prediction Models-A Case Study  \nBashar Alfalah, P. Eng  \nMember ASHRAE  \nABSTRACT  \nMehdi Shahrestani, PhD Li Shao, PhD  \nMember ASHRAE  \nThere is a necessity to reduce energy consumption in the building sector to mitigate the associated carbon emissions, which, globally, accountfor 39% oftotal emissions. Severalfactors have to be considered in the effort to reduce energy consumption. Among them, building occupancy is one of the key drivers in the operation of building services and hence leading to energy consumption and its associated carbon emissions. Information regarding building occupancy at different times of the day or year could potentially help to enhance the energy and environmental performance of buildings. This study investigated the performance of two machine learning models (Hidden Markov Model and Artificial Neural Network) in predicting the occupancy numbers ofa highdensity higher education building in England that was used as the case study. The models were developed using high-resolution actual occupancy data obtained atfive-minute intervals for 12 months, covering workdays, weekends, holidays, and vacations. Occupancy data were collected using high-accuracy infrared video image sensors to cover the gap in the paucity of occupancy data in such buildings and to eliminate the uncertainties faced in previous studies. Several statistical analyses, such as principal component analysis and cross-validation, were conducted to ensure that optimal inputs are usedfor developing and evaluating the models. The results of the two occupancy prediction models developed indicated that the Hidden Markov Modelperforms better than the Artificial Neural Network in predicting occupancy numbers.  \nINTRODUCTION  \nBuilding operation schedules vary based on the building type, activity, and opening hours. Buildings such as higher education institution libraries have fixed schedules for building operating systems, with the assumption that the building is fully occupied. For instance, HVAC systems for all zones operate from early morning to the end of the working day, which could lead to significant energy wastage when the building is partially occupied. Such practices could be attributed to a lack of knowledge of occupancy numbers, patterns, and schedules in the building during different periods. This isone of the reasons for the growing interest over the last decade in studies on collecting, estimating, and predicting building occupancy numbers. In addition, the occupancy in certain building types, such as educational buildings, has not been sufficiently examined.  \nSeveral studies have developed machine learning models, such as the Hidden","cbCaikRZLaT7p798","https://ap.wps.com/l/cbCaikRZLaT7p798","pdf",2098442,1,11,"English","en",105,"# Abstract\n# Introduction\n## Motivation and research gap\n## Related work on HMM and ANN","[{\"question\":\"Why is building occupancy important for reducing energy consumption?\",\"answer\":\"Building occupancy strongly influences building services operation and therefore energy use and related carbon emissions. Access to occupancy patterns across different times can improve energy and environmental performance.\"},{\"question\":\"Which machine learning models are compared in the case study?\",\"answer\":\"The study investigates two models: Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for predicting occupancy numbers.\"},{\"question\":\"What data were used to develop and evaluate the models?\",\"answer\":\"The models were built using high-resolution actual occupancy data collected at five-minute intervals over 12 months, covering workdays, weekends, holidays, and vacations, using high-accuracy infrared video image sensors.\"}]","Machine Learning Occupancy Prediction Models - A Case Study | PDF",1785671994,28,{"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},"machine-learning-occupancy-prediction-models-a-case-study","",{"@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/machine-learning-occupancy-prediction-models-a-case-study/116834/",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","2026-08-02",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 building occupancy important for reducing energy consumption?","Question",{"text":76,"@type":77},"Building occupancy strongly influences building services operation and therefore energy use and related carbon emissions. Access to occupancy patterns across different times can improve energy and environmental performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared in the case study?",{"text":81,"@type":77},"The study investigates two models: Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for predicting occupancy numbers.",{"name":83,"@type":74,"acceptedAnswer":84},"What data were used to develop and evaluate the models?",{"text":85,"@type":77},"The models were built using high-resolution actual occupancy data collected at five-minute intervals over 12 months, covering workdays, weekends, holidays, and vacations, using high-accuracy infrared video image sensors.","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"]