IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v264y2023ics0360544222031607.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222031607
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    4. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    5. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    6. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    7. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    8. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    9. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    10. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    11. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).
    12. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    13. Han, Xiaojuan & Wei, Zixuan & Hong, Zhenpeng & Zhao, Song, 2020. "Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain," Renewable Energy, Elsevier, vol. 161(C), pages 419-434.
    14. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    15. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    16. Ramadhani, Farah & Hussain, M.A. & Mokhlis, Hazlie & Fazly, Muhamad & Ali, Jarinah Mohd., 2019. "Evaluation of solid oxide fuel cell based polygeneration system in residential areas integrating with electric charging and hydrogen fueling stations for vehicles," Applied Energy, Elsevier, vol. 238(C), pages 1373-1388.
    17. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    18. Yang, G. & Zhai, X.Q., 2019. "Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition," Energy, Elsevier, vol. 174(C), pages 647-663.
    19. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    20. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    21. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    22. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    23. Zhang, Xiaofeng & Yan, Renshi & Zeng, Rong & Zhu, Ruilin & Kong, Xiaoying & He, Yecong & Li, Hongqiang, 2022. "Integrated performance optimization of a biomass-based hybrid hydrogen/thermal energy storage system for building and hydrogen vehicles," Renewable Energy, Elsevier, vol. 187(C), pages 801-818.
    24. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    25. Guo, Jiacheng & Zhang, Peiwen & Wu, Di & Liu, Zhijian & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua, 2022. "Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting," Energy, Elsevier, vol. 239(PC).
    26. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    27. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    2. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    3. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    5. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    6. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    7. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    8. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    9. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    10. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    11. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    12. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    13. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    14. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    15. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
    16. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    17. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    18. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    19. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    20. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222031607. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.