In an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.
{"title":"Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning","authors":"Juan Yang, Jing Yu, Shijing Wang","doi":"10.1002/adc2.190","DOIUrl":"https://doi.org/10.1002/adc2.190","url":null,"abstract":"In an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.","PeriodicalId":505272,"journal":{"name":"Advanced Control for Applications","volume":"60 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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