[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120312-en":3,"doc-seo-120312-105":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},120312,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Predicting Building Primary Energy Use Based on Machine Learning - Evidence from Portland - Research Article","Accurately predicting equivalent primary energy use (EPEU) in buildings is crucial for advancing energy-efficient design, optimizing operational strategies, and achieving sustainability targets in the built environment. This study builds reliable EPEU prediction models using a comprehensive dataset from buildings in Portland, USA. A structured machine learning workflow covers feature selection, preprocessing, model training, and evaluation, with Random Forest, GBDT, and Back-Propagation Neural Networks compared across building types. Ensemble methods, especially RF and GBDT, show superior accuracy, robustness, and stability, supporting data-driven decisions for improved energy performance and lower carbon emissions.","Engineering Technology  \nISSN (online): 2409-9821  \nPredicting Building Primary Energy Use Based on Machine Learning: Evidence from Portland  \nYin Junjia*, Aidi Hizami Alias, Nuzul Azam Haron and Nabilah Abu Bakar Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia  \nARTICLE INFO  \nArticle Type: Research Article  \nAcademic Editor: Abdelhakim Mesloub Keywords:  \nPrimary energy use Sustainability optimization Building energy prediction  \nMachine learning algorithms Timeline:  \nReceived: November 03, 2024  \nAccepted: December 21, 2024  \nPublished: December 28, 2024  \nCitation: Junjia Y, Alias AH, Haron NA, Abu-Bakar N. Predicting building primary energy use based on machine learning: Evidence from Portland. Int JArchit Eng Technol. 2024; 11: 124-139.  \nDOI: [https://doi.org/10.15377/2409-9821.2024.11.7](https://doi.org/10.15377/2409-9821.2024.11.7)  \nABSTRACT  \nAccurately predicting equivalent primary energy use (EPEU) in buildings is crucial for advancing energy-efficient design, optimizing operational strategies, and achieving sustainability goals in the built environment. This study aims to develop reliable prediction models for EPEU by leveraging a comprehensive and high-quality dataset from buildings in Portland, USA. To achieve this, a systematic machine learning framework is adopted, encompassing feature selection, data preprocessing, model training, and performance evaluation. Several state-of-the-art machine learning algorithms are applied, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Back-Propagation Neural Networks (BP) . These models are trained using key features such as building type, gross floor area, construction year, and various operational characteristics that are known to significantly influence energy consumption patterns. The dataset is carefully cleaned and normalized to ensure model generalizability and minimize bias. Model performance is assessed using standard statistical metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) . Among the tested models, ensemble learning methods—particularly RF and GBDT—consistently outperform others in terms of prediction accuracy, robustness, and stability across different building types. The results of this study not only highlight the potential of machine learning in energy prediction tasks but also provide actionable insights for architects, engineers, facility managers, and policymakers. By identifying the most influential variables and employing effective predictive models, this research supports data-driven decision-making processes aimed at improving building energy performance. Ultimately, the findings contribute to broader efforts in reducing carbon emissions and facilitating the transition toward more sustainable and energy-resilient urban environments.  \n*Corresponding Author  \n[Email: gs64764@student.upm.edu.my](Email: gs64764@student.upm.edu.my)  \n[Tel:](Tel:) +(86) 18908453651  \n©2024 Junjia et al. Published by Avanti Publishers. This is an open access article licensed under the terms of the Creative Commons Attribution NonCommercial License which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. ([http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/))  \n1. Introduction  \nThe building sector is one of the largest energy consumers worldwide, accounting for a significant share of global primary energy use and carbon emissions. In the U.S., buildings account for 40% of total energy demand [1] . Accurate prediction of equivalent primary energy use (EPEU) is essential for optimizing building energy performance, informing policy decisions, and achieving sustainability goals. Fig. (1) illustrates the enormous consumption of primary energy in the U.S.'s residential, commercial, and industrial building sector","cbCaicDWVDJLxtgy","https://ap.wps.com/l/cbCaicDWVDJLxtgy","pdf",641750,1,16,"English","en",105,"# Introduction\n## Building energy demand and the need for EPEU prediction\n## Energy sources and modeling background\n## Machine learning approaches and knowledge gap\n# Methods","[{\"question\":\"What does the study aim to predict in building energy use?\",\"answer\":\"The study aims to predict equivalent primary energy use (EPEU) in buildings using machine learning models.\"},{\"question\":\"Which machine learning algorithms are evaluated?\",\"answer\":\"Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Back-Propagation Neural Networks (BP) are tested and compared.\"},{\"question\":\"What kinds of inputs are used to train the models?\",\"answer\":\"Models use key building attributes such as building type, gross floor area, construction year, and multiple operational characteristics tied to energy consumption patterns.\"}]","Predicting Building Primary Energy Use Based on Machine Learning - 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