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Medium-term heat load prediction for an existing residential building based on a wireless on-off control system

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  • Gu, Jihao
  • Wang, Jin
  • Qi, Chengying
  • Min, Chunhua
  • Sundén, Bengt

Abstract

For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are considered as influencing factors. It is found that the prediction accuracies of the ELM and GA-BP are slightly higher than that of WNN, so the ELM and GA-BP models provide feasible methods for the heat load prediction. The SVM shows smaller relative errors in the model prediction compared with three neural network algorithms.

Suggested Citation

  • Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:709-718
    DOI: 10.1016/j.energy.2018.03.179
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    8. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    9. Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
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    14. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    15. Yuan, Jianjuan & Huang, Ke & Lu, Shilei & Zhang, Ji & Han, Zhao & Zhou, Zhihua, 2022. "Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study," Energy, Elsevier, vol. 238(PC).
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    18. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).

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