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Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach

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  • Yang, Ting
  • Zhao, Liyuan
  • Li, Wei
  • Wu, Jianzhong
  • Zomaya, Albert Y.

Abstract

Indoor environmental quality is an important issue since people spend most of their time indoors. This paper aims to develop an autonomous indoor environment management approach to ensure a healthy indoor environment with minimized energy cost via the optimal control of ventilation system and heating/cooling system in smart homes. Due to the uncertainties of weather conditions, electricity price and home occupancy, as well as the complex interaction between indoor air quality and indoor thermal environment, it is challenging to develop an efficient control strategy. To address this challenge, the indoor environment management problem is formulated as a Markov decision process, and then a deep reinforcement learning control strategy, which combines double deep Q network with prioritized experience replay mechanism, is proposed to solve the Markov decision process. The proposed approach can make adaptive control decisions based on the current observations without requiring any forecast information of system uncertainties. Control performance under different scenarios show the proposed approach has good adaptability to the variation of weather conditions, electricity prices, home occupancy patterns and indoor temperature requirements. Moreover, the proposed approach is compared with a double deep Q network-based approach and a model predictive control-based approach. Comparison results show that the proposed approach reduces the average daily energy cost by 3.51% and 8.56% in winter scenarios and 4.05%, 7.88% in summer scenarios while achieving the smallest indoor air quality and temperature violations.

Suggested Citation

  • Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007431
    DOI: 10.1016/j.apenergy.2021.117335
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    2. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    3. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
    4. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    5. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
    6. Víctor Sanz i López & Ramon Costa-Castelló & Carles Batlle, 2022. "Literature Review of Energy Management in Combined Heat and Power Systems Based on High-Temperature Proton Exchange Membrane Fuel Cells for Residential Comfort Applications," Energies, MDPI, vol. 15(17), pages 1-22, September.
    7. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    8. Seppo Sierla & Heikki Ihasalo & Valeriy Vyatkin, 2022. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems," Energies, MDPI, vol. 15(10), pages 1-25, May.
    9. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    10. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    11. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).

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