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Netw."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            In recent years, the focus has been on enhancing user comfort in commercial buildings while cutting energy costs. Efforts have mainly centered on improving HVAC systems, the central control system. However, it\u2019s evident that HVAC alone can\u2019t ensure occupant comfort. Lighting, blinds, and windows, often overlooked, also impact energy use and comfort. This paper introduces a holistic approach to managing the delicate balance between energy efficiency and occupant comfort in commercial buildings. We present\n            <jats:italic>OCTOPUS<\/jats:italic>\n            , a system employing a deep reinforcement learning (DRL) framework using data-driven techniques to optimize control sequences for all building subsystems, including HVAC, lighting, blinds, and windows.\n            <jats:italic>OCTOPUS<\/jats:italic>\n            \u2019s DRL architecture features a unique reward function facilitating the exploration of tradeoffs between energy usage and user comfort, effectively addressing the high-dimensional control problem resulting from interactions among these four building subsystems. To meet data training requirements, we emphasize the importance of calibrated simulations that closely replicate target-building operational conditions. We train\n            <jats:italic>OCTOPUS<\/jats:italic>\n            using 10-year weather data and a calibrated building model in the EnergyPlus simulator. Extensive simulations demonstrate that\n            <jats:italic>OCTOPUS<\/jats:italic>\n            achieves substantial energy savings, outperforming state-of-the-art rule-based and DRL-based methods by 14.26% and 8.1%, respectively, in a LEED Gold Certified building while maintaining desired human comfort levels.\n          <\/jats:p>","DOI":"10.1145\/3656043","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T13:41:38Z","timestamp":1712065298000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Exploring Deep Reinforcement Learning for Holistic Smart Building Control"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6114-2801","authenticated-orcid":false,"given":"Xianzhong","family":"Ding","sequence":"first","affiliation":[{"name":"University of California Merced School of Engineering,  Merced, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4531-9704","authenticated-orcid":false,"given":"Alberto","family":"Cerpa","sequence":"additional","affiliation":[{"name":"University of California, Merced, Merced, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2732-6954","authenticated-orcid":false,"given":"Wan","family":"Du","sequence":"additional","affiliation":[{"name":"University of California Merced School of Engineering,  Merced, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"sketchup","year":"2018","unstructured":"2018. sketchup. https:\/\/www.sketchup.com"},{"key":"e_1_3_1_3_2","volume-title":"GEZE: Products, System Solutions and Services for Doors and Windows","year":"2019","unstructured":"2019. 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