Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine

被引:0
|
作者
Shen, Yang [1 ]
Li, Deyi [2 ]
Wang, Wenbo [2 ]
机构
[1] Wuhan Univ Sci & Technol, Coll Sci, Wuhan 430081, Peoples R China
[2] Wuhan Univ Sci & Technol, Hubei Prov Key Lab Syst Sci Met Proc, Wuhan 430065, Peoples R China
关键词
multi-energy load prediction; integrated energy system; comprehensive weight method; fennec fox optimization algorithm; hybrid kernel extreme learning machine;
D O I
10.3390/e26080699
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems
    Li, Chuang
    Li, Guojie
    Wang, Keyou
    Han, Bei
    [J]. ENERGY, 2022, 259
  • [32] Operational characteristics of an integrated island energy system based on multi-energy complementarity
    Lin, Jianhui
    Gu, Yujiong
    Wang, Zijie
    Zhao, Ziliang
    Zhu, Ping
    [J]. RENEWABLE ENERGY, 2024, 230
  • [33] Research on operation characteristics and optimization of a multi-energy complementary integrated system
    Li, Lan
    Liu, Zhiqiang
    Liu, Jiaxing
    [J]. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2020, 41 (07): : 49 - 56
  • [34] Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm
    Dodo, Usman Alhaji
    Ashigwuike, Evans Chinemezu
    Emechebea, Jonas Nwachukwu
    Abbac, Sani Isah
    [J]. ENERGY NEXUS, 2022, 8
  • [35] Regional integrated energy system long-term planning optimization based on multi-energy complementarity quantification
    Lin, Xiaojie
    Zhang, Nan
    Zhong, Wei
    Kong, Fanqi
    Cong, Feiyun
    [J]. JOURNAL OF BUILDING ENGINEERING, 2023, 68
  • [36] Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks
    Wang, Jiangjiang
    Deng, Hongda
    Qi, Xiaoling
    [J]. ENERGY, 2022, 261
  • [37] Automated machine learning-based building energy load prediction method
    Zhang, Chaobo
    Tian, Xiangning
    Zhao, Yang
    Lu, Jie
    [J]. JOURNAL OF BUILDING ENGINEERING, 2023, 80
  • [38] An integrated impact localization method for thermal protection structure based on hybrid kernel extreme learning machine
    Chao, Zhang
    Cheng, Xu
    Wuqiang, Tang
    Yupeng, Zhang
    Chongcong, Tao
    Jinhao, Qiu
    [J]. Journal of Intelligent Material Systems and Structures, 2024, 35 (19) : 1483 - 1495
  • [39] Research on Multi-Energy Integrated Ship Energy Management System Based on Hierarchical Control Collaborative Optimization Strategy
    Ren, Yuanjie
    Zhang, Lanyong
    Shi, Peng
    Zhang, Ziqi
    [J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (10)
  • [40] Improved multi-energy flow calculation method for integrated energy system considering initial value optimization of natural gas system
    Wang S.
    Zhang S.
    Cheng H.
    Yuan K.
    Song Y.
    Han F.
    [J]. Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2022, 42 (01): : 28 - 36