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A load forecasting model based on support vector regression with whale optimization algorithm

  • 1222: Intelligent Multimedia Data Analytics and Computing
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Abstract

Power load forecasting is an important part of smart grid, and its accuracy will directly affect the control and planning of power system operation. In the context of electricity market reform, real-time electricity prices affect users’ electricity consumption patterns. A short-term load forecasting model based on support vector regression (SVR) with whale optimization algorithm (WOA) considering real-time electricity price is proposed in this paper. Meta-heuristics are very promising in optimizing the parameters of SVR, and the WOA algorithm is used to determine the appropriate combination of SVR’s parameters to accurately establish a forecasting model. The initial value of the original WOA algorithm lacks ergodicity, and has defects such as easy to fall into local optimum and low convergence accuracy. Chaos mechanism and elite opposition-based learning strategy are introduced into WOA to balance the exploration and exploitation of the algorithm and improve the algorithm convergence speed. Numerical examples involving two power load datasets show that the proposed model can achieve better forecasting performance in comparison with other models, such as SVR, BPNN. At the same time, it proves that the forecasting accuracy with electricity price is higher than that without electricity price.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grants No. 62062007.

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Authors and Affiliations

  1. School of Electrical Engineering, Guangxi University, Nanning, 530004, China

    Yuting Lu

  2. School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China

    Gaocai Wang

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  1. Yuting Lu
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Correspondence to Gaocai Wang.

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Lu, Y., Wang, G. A load forecasting model based on support vector regression with whale optimization algorithm. Multimed Tools Appl 82, 9939–9959 (2023). https://doi.org/10.1007/s11042-022-13462-2

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