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BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures

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  • Wan, Taocheng
  • Bai, Yan
  • Wang, Tingxiang
  • Wei, Zhuo

Abstract

An efficient method is used to optimize indoor air distribution in heating systems of stratum ventilation to be beneficial to develop a potential of energy-saving with providing proper thermal comfort. To save much energy with providing proper thermal comfort, asearch for good optimization methods or strategies is required to optimize indoor air distribution affected the parameters (i.e., supply air temperature, supply vane angle, clothing insulation and outdoor air temperature) in stratum ventilation systems. In this study, the air distribution of stratum ventilation for heating is optimized by using the strategy 1 (the optimal strategy of the predicted mean vote (PMV) between −0.7 and 0.7 based on a technique of order preference by similarity to ideal solution (TOPSIS)) and the strategy 2 (the optimal strategy of PMV between −0.7 and 0.7, and draft rate (DR) less than 20). Firstly, the Computational Fluid Dynamics software is used to perform ventilation operations on the parameters for stratum ventilation systems. Secondly, according to the simulation results, the parameters are set as the input variables to construct the predictive model of ventilation performance (i.e., Energy consumption, PMV and DR) by using the Back Propagation Neural Network (BPNN) method and response surface model (RSM) method, respectively. According to the accuracy analysis of models, the BPNN method is chosen to build the predictive model. Finally, the ventilation performance is output through the optimization of the proposed BPNN-based strategy 1 and strategy 2. The optimized result shows that BPNN can build prediction models of great precision for ventilation performance. The proposed strategy 2 can save more energy by providing thermal comfort than the strategy 1. And the strategies can be beneficial to save energy as the outdoor air temperature varies. Moreover, the clothing insulation instead of supply air temperature indirectly saves energy, while providing proper thermal comfort.

Suggested Citation

  • Wan, Taocheng & Bai, Yan & Wang, Tingxiang & Wei, Zhuo, 2022. "BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures," Applied Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922003257
    DOI: 10.1016/j.apenergy.2022.118899
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    Cited by:

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    2. Zhao, Yihang & Dan, Dan & Zheng, Siyu & Wei, Mingshan & Xie, Yi, 2023. "A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion," Energy, Elsevier, vol. 267(C).
    3. Jiaqi Cao & Shiyu Zhou & Tao Wang & Baoqi Shan & Xueping Liu, 2023. "Research on a Variable Water Supply Temperature Strategy for a Ground-Source Heat Pump System Based on TRNSYS-GENOPT (TRNOPT) Optimization," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    4. Chenhao Sun & Xiwei Jiang & Zhiwei Jia & Kun Yu & Sheng Xiang & Jianhong Su, 2023. "An Optimization Ensemble for Integrated Energy System Configuration Strategy Incorporating Demand–Supply Coordination," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    5. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.

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