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A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system

Author

Listed:
  • Xie, Yiwei
  • Hu, Pingfang
  • Zhu, Na
  • Lei, Fei
  • Xing, Lu
  • Xu, Linghong
  • Sun, Qiming

Abstract

This paper proposed a hybrid hour-ahead forecast model, which combines multiple superimposed long and short term memory (LSTM) network and back-propagation neural network (BPNN), for the purpose of the load forecast on a building-level. The model (hybrid LSTM-BPNN) takes full consideration of the influence of the meteorological information, time information, the information of persons and devices and indoor thermal parameters. A method (LSTM-BPNN + BPNN) is further developed based on the forecasting advantages of sensible heat by hybrid LSTM-BPNN and latent heat by BPNN, in which the cooling load was split into sensible heat and latent heat to forecast separately. The results show that the method achieves the best performance for hour-ahead load forecasting compared with several classic forecasting models. An operation strategy based on the proposed LSTM-BPNN + BPNN method and LSTM model is applied to an improved ground source heat pump system integrated with cooling storage (GSHPs-CS) which was equipped with two storage tanks to achieve the variable volume control of stored chilled water. The system model is established by TRNSYS. Compared to conventional GSHPs-CS, the energy consumption and operating cost of the improved GSHPs-CS system were reduced by 11.5% and 7.5%, respectively.

Suggested Citation

  • Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:1244-1259
    DOI: 10.1016/j.renene.2020.07.142
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    References listed on IDEAS

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    1. Gang, Wenjie & Wang, Jinbo & Wang, Shengwei, 2014. "Performance analysis of hybrid ground source heat pump systems based on ANN predictive control," Applied Energy, Elsevier, vol. 136(C), pages 1138-1144.
    2. Gang, Wenjie & Wang, Jinbo, 2013. "Predictive ANN models of ground heat exchanger for the control of hybrid ground source heat pump systems," Applied Energy, Elsevier, vol. 112(C), pages 1146-1153.
    3. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    4. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    5. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    6. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    7. Thygesen, Richard & Karlsson, Björn, 2016. "Simulation of a proposed novel weather forecast control for ground source heat pumps as a mean to evaluate the feasibility of forecast controls’ influence on the photovoltaic electricity self-consumpt," Applied Energy, Elsevier, vol. 164(C), pages 579-589.
    8. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    9. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    10. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong, 2020. "Collaborative optimization of ground source heat pump-radiant ceiling air conditioning system based on response surface method and NSGA-II," Renewable Energy, Elsevier, vol. 147(P1), pages 249-264.
    11. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    12. Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
    13. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    14. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    15. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
    16. Petersen, Steffen & Bundgaard, Katrine Wieck, 2014. "The effect of weather forecast uncertainty on a predictive control concept for building systems operation," Applied Energy, Elsevier, vol. 116(C), pages 311-321.
    17. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
    Full references (including those not matched with items on IDEAS)

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    2. Xie, Yiwei & Hu, Pingfang & Peng, Donggen & Zhu, Na & Lei, Fei, 2023. "Development of a group control strategy based on multi-step load forecasting and its application in hybrid ground source heat pump," Energy, Elsevier, vol. 273(C).
    3. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
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