{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T19:13:23Z","timestamp":1769627603762,"version":"3.49.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.<\/jats:p>","DOI":"10.3233\/jifs-233544","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T11:30:26Z","timestamp":1708687826000},"page":"9029-9042","source":"Crossref","is-referenced-by-count":0,"title":["HVAC energy consumption prediction based on RJITL deep neural network model"],"prefix":"10.1177","volume":"46","author":[{"given":"Xiaoli","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing University of Technology, Beijing, China"}]},{"given":"Linhui","family":"Du","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Xiaowei","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Kang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Yongkang","family":"Hu","sequence":"additional","affiliation":[{"name":"Instrumentation Technology & Economy Institute, Beijing, China"}]}],"member":"179","reference":[{"issue":"19","key":"10.3233\/JIFS-233544_ref1","doi-asserted-by":"crossref","first-page":"7231","DOI":"10.3390\/en15197231","article-title":"Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends[J]","volume":"15","author":"Kim","year":"2022","journal-title":"Energies"},{"key":"10.3233\/JIFS-233544_ref2","doi-asserted-by":"crossref","first-page":"109465","DOI":"10.1016\/j.enbuild.2019.109465","article-title":"Energy performance assessment of HVAC commissioning using long-term monitoring data: A case study of the newly built office building in South Korea[J]","volume":"204","author":"Kim","year":"2019","journal-title":"Energy and Buildings"},{"key":"10.3233\/JIFS-233544_ref3","doi-asserted-by":"crossref","first-page":"111909","DOI":"10.1016\/j.enbuild.2022.111909","article-title":"GEIN: An interretable benchmarking framework towards all building types based on machine learning[J]","volume":"260","author":"Jin","year":"2022","journal-title":"Energy and Buildings"},{"key":"10.3233\/JIFS-233544_ref4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-3-642-39578-9_12","article-title":"Dynamic Modeling of HVAC System with State-Space Method[C]","author":"Yao","year":"2014","journal-title":"Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning"},{"key":"10.3233\/JIFS-233544_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.enbuild.2017.06.008","article-title":"Residential HVAC fault detection using a system identification approach[J]","volume":"151","author":"Turner","year":"2017","journal-title":"Energy and Buildings"},{"issue":"1","key":"10.3233\/JIFS-233544_ref6","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/10789669.2013.836877","article-title":"Recent advances in dynamic modeling of HVAC equipment. 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