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Integrated energy system evaluation method based on dimensionality reduction and indexes updating with incomplete information

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  • Leng, Ya-Jun
  • Peng, Dan
  • Zhang, Huan

Abstract

The integrated energy system (IES) can improve energy efficiency and promote the use of renewable energy. It is of great significance to find the optimal alternative from many IES alternatives. However, most studies focused on the IES evaluation with complete information. Given this background, this paper proposes an evaluation method based on generalized regression neural network (GRNN) and probabilistic neural network (PNN) to address the issue of IES evaluation with incomplete information, which takes the running speed of the algorithm and the dynamic updating of the indexes into account. Firstly, the missing data is predicted via GRNN, and a complete evaluation matrix is obtained. Then, the evaluation indexes are clustered by affinity propagation algorithm, and the evaluation matrix of the cluster centers is determined, which can reduce the dimension of the initial matrix and improve the speed of the IES evaluation method. For the newly added evaluation indexes, the PNN is used to classify them, and the new cluster center is calculated, which can address the issue of the indexes updating. Based on the data of a hospital in Henan, case studies were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than the existing IES evaluation method.

Suggested Citation

  • Leng, Ya-Jun & Peng, Dan & Zhang, Huan, 2023. "Integrated energy system evaluation method based on dimensionality reduction and indexes updating with incomplete information," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223009465
    DOI: 10.1016/j.energy.2023.127552
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    2. Leng, Ya-Jun & Li, Xiao-Shuang & Zhang, Huan, 2024. "NSGA-T: A novel evaluation method for renewable energy plans," Energy, Elsevier, vol. 290(C).
    3. Xin Wang & Fan Xiao & Chunming Tu & Qi Guo & Yuchao Hou & Zheng Lan, 2024. "Dynamic Assessment of Photovoltaic-Storage Integrated Energy Stations Health Incorporating Subjective and Objective Characteristics," Sustainability, MDPI, vol. 16(4), pages 1-25, February.

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