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Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior

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  • Zhang, Xiaofeng
  • Kong, Xiaoying
  • Yan, Renshi
  • Liu, Yuting
  • Xia, Peng
  • Sun, Xiaoqin
  • Zeng, Rong
  • Li, Hongqiang

Abstract

The access of electric vehicles facilitates in the fluctuation and diversification of building load, accurate load prediction contributes to investigating the operation and optimization of energy supply systems for building integrated with electric vehicles (EVs). This study proposes a multivariate load prediction model for building/EVs considering occupant travel behavior. Firstly, travel variables of dissimilar occupants are obtained by fitting distribution, Markov chain and machine learning method. Besides, these variables are sampled to construct the occupant travel behavior model based on Monte Carlo method. In addition, occupancy rate and hourly load of different buildings are determined according to the occupant travel behavior. Ultimately, data-driven approaches, such as artificial neural network, long short term memory network (LSTM) and LSTM with temporal pattern attention (TPA-LSTM), are applied to construct the load prediction model for building/electric vehicles. Results indicated that building load constructed in accordance with the occupancy rate of occupants' travel behavior exhibits a favorable cooperativity with the EVs charging load. TPA-LSTM model has a high prediction accuracy, and the maximum correlation coefficient reaches 0.987. In general, this study provides an effective tool for accurate load prediction of building integrated with EVs.

Suggested Citation

  • Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222031607
    DOI: 10.1016/j.energy.2022.126274
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    Cited by:

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    2. Song, Cairong & Yang, Haidong & Cai, Jianyang & Yang, Pan & Bao, Hao & Xu, Kangkang & Meng, Xian-Bing, 2024. "Multi-energy load forecasting via hierarchical multi-task learning and spatiotemporal attention," Applied Energy, Elsevier, vol. 373(C).
    3. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    4. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    5. Salari, Azam & Zeinali, Mahdi & Marzband, Mousa, 2024. "Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior," Energy, Elsevier, vol. 288(C).
    6. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).

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