[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124095-en":3,"doc-seo-124095-105":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},124095,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Baseline Energy Model (MLBEM) - Evaluate Prediction Performances in Building Energy Consumption","Electric Energy Consumption (EEC) prediction for building operations can be supported by a Baseline Energy Model (BEM), ensuring efficient estimation using relevant independent variables. Developing a BEM is challenging because EEC varies over time in an educational building. This study compares deep learning, machine learning, and statistical approaches to build a BEM and forecast EEC for 24–96 hours, incorporating lecture-week operations and student/staff counts. Residual error, correlation coefficient, R², and training time are used. SVR achieves the best performance and is recommended as a universal MLBEM component to help operators monitor, plan, and manage energy use while reducing utility costs.","Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction Performances in Building Energy Consumption  \nRijalul Fahmi Mustapa  \nCollege of Engineering, Universiti Teknologi MARA, Johor Branch, 81750 Masai, Johor, Malaysia [rijalulfahmi@uitm.edu.my](rijalulfahmi@uitm.edu.my)  \nMuhammad Asraf Hairuddin  \nCollege of Engineering, Universiti Teknologi MARA, Johor Branch, 81750 Masai, Johor, Malaysia | Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam 40450, Malaysia  \n[masraf@uitm.edu.my](masraf@uitm.edu.my) (corresponding author)  \nAtiqah Hamizah Mohd Nordin  \nCollege of Engineering, Universiti Teknologi MARA Johor Branch, 81750 Masai, Johor, Malaysia [atiqah26@uitm.edu.my](atiqah26@uitm.edu.my)  \nNofri Yenita Dahlan  \nSolar Research Institute, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia[nofriyenita012@uitm.edu.my](nofriyenita012@uitm.edu.my)  \nIhsan Mohd Yassin  \nMicrowave Research Institute, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia [ihsan_yassin@uitm.edu.my](ihsan_yassin@uitm.edu.my)  \nNur Dalila Khirul Ashar  \nDepartment of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia | College of Engineering, Universiti Teknologi MARA, Johor Branch, 81750 Masai, Johor, Malaysia  \n[nurdalila306@uitm.edu.my](nurdalila306@uitm.edu.my)  \nReceived: 1 May 2024 | Revised: 17 June 2024 | Accepted: 20 June 2024  \nLicensed under a CC-BY 4.0 license | Copyright (c) by the authors | DOI: [https://doi.org/10.48084/etasr.7683](https://doi.org/10.48084/etasr.7683)  \nABSTRACT  \nElectric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also taken into account. Three models with different categories involving Long Short-Term Memory (LSTM),  \n[www.etasr.com](www.etasr.com) Mustapa etal.: Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction…  \nSupport Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills.  \nKeywords-energy efficiency forecast; machine learning; deep learning; baseline model; buildings  \nI. INTRODUCTION  \nThe surge in the expansion of new infrastructure buildings is expected to lead to an increase in Electric Energy Consumption (EEC), and the energy demand from buildings is expected to increase by 50% by 2050. This accounts for factors, such as population growth, urbanization, and higher living standards contributing to higher greenhouse gas emissions. The trend of increasing EEC will greatly impact greenhouse gas production. The International E","cbCaibeICRDDNgsN","https://ap.wps.com/l/cbCaibeICRDDNgsN","pdf",1534815,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and need for Baseline Energy Models\n## Study objective and modeling approach","[{\"question\":\"What is the role of a Baseline Energy Model (BEM) in building energy prediction?\",\"answer\":\"A BEM represents Electric Energy Consumption (EEC) using independent variables. It supports accurate forecasting of future EEC based on patterns in the dataset.\"},{\"question\":\"Which modeling approaches are compared to develop the MLBEM?\",\"answer\":\"The study compares deep learning, machine learning, and statistical models, including Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and ARIMAX.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is assessed using residual error evaluation and metrics including the correlation coefficient, coefficient of determination (R²), and training time.\"}]","Machine Learning Baseline Energy Model (MLBEM) - Evaluate Prediction Performances in Building Energy Consumption | PDF",1785820295,23,{"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-baseline-energy-model-mlbem-evaluate-prediction-performances-in-building-energy-consumption","",{"@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-baseline-energy-model-mlbem-evaluate-prediction-performances-in-building-energy-consumption/124095/",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},"What is the role of a Baseline Energy Model (BEM) in building energy prediction?","Question",{"text":76,"@type":77},"A BEM represents Electric Energy Consumption (EEC) using independent variables. It supports accurate forecasting of future EEC based on patterns in the dataset.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which modeling approaches are compared to develop the MLBEM?",{"text":81,"@type":77},"The study compares deep learning, machine learning, and statistical models, including Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and ARIMAX.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated in the study?",{"text":85,"@type":77},"Performance is assessed using residual error evaluation and metrics including the correlation coefficient, coefficient of determination (R²), and training time.","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},[94,98,102,106,111,116,121,124,128,131,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]