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Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Author

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  • Xiaoyu Gao

    (School of Energy and Environment Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Chengying Qi

    (School of Energy and Environment Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Guixiang Xue

    (School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China)

  • Jiancai Song

    (School of Information and Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Yahui Zhang

    (School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
    School of Science, Hebei University of Technology, Tianjin 300401, China)

  • Shi-ang Yu

    (School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
    School of Science, Hebei University of Technology, Tianjin 300401, China)

Abstract

The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users’ needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings’ heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings’ heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019–2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting.

Suggested Citation

  • Xiaoyu Gao & Chengying Qi & Guixiang Xue & Jiancai Song & Yahui Zhang & Shi-ang Yu, 2020. "Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR," Energies, MDPI, vol. 13(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6079-:d:448614
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. Shangfu Wei & Xiaoqing Bai, 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network," Energies, MDPI, vol. 15(5), pages 1-21, February.
    5. Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).

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