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

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

Energy, 2023, vol. 264, issue C

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.

Keywords: Building/electric vehicle interaction; Load prediction; Personnel travel behavior; Data-driven approach; Cooling; Heating and electricity; Probabilistic model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:264:y:2023:i:c:s0360544222031607

DOI: 10.1016/j.energy.2022.126274

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