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A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Author

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  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shuyu Dai

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

Suggested Citation

  • Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:408-:d:93647
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    References listed on IDEAS

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    3. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Ze Yuan & Weizhou Wang & Jing Peng & Fuchao Liu & Jianhua Yang, 2017. "Case Library Construction Technology of Energy Loss in Distribution Networks Considering Regional Differentiation Theory," Energies, MDPI, vol. 10(11), pages 1-14, November.
    5. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
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    7. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    8. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
    9. Huiru Zhao & Guo Huang & Ning Yan, 2018. "Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China," Energies, MDPI, vol. 11(4), pages 1-21, March.
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    11. Renxi Gong & Xianglong Li, 2023. "A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism," Energies, MDPI, vol. 16(6), pages 1-24, March.
    12. Rafik Nafkha & Tomasz Ząbkowski & Krzysztof Gajowniczek, 2021. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers," Energies, MDPI, vol. 14(8), pages 1-17, April.
    13. Hongfeng Li & Lifeng Cui & Zigang Ma & Bin Li, 2020. "Multi-Objective Optimization of the Halbach Array Permanent Magnet Spherical Motor Based on Support Vector Machine," Energies, MDPI, vol. 13(21), pages 1-20, October.
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