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A new short-term load forecasting method of power system based on EEMD and SS-PSO

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Abstract

Aiming to the disadvantages of short-term load forecasting with empirical mode decomposition (EMD) such as mode mixing and many high-frequency random components, a new short-term load forecasting model based on ensemble empirical mode decomposition (EEMD) and sub-section particle swarm optimization (SS-PSO) is proposed in this paper. Firstly, the load sequence is decomposed into a limited number of intrinsic mode function (IMF) components and one remainder by EEMD, which can avoid the mode mixing problem of traditional EMD. Then, through calculating and observing the spectrum of decomposed series, some low-frequency IMFs are extracted and reconstructed. Other IMFs can be forecasted with appropriate forecasting models. Since IMF1 is main random component of the load sequence, the linear combination model is adopted to forecast IMF1. Because the weights of the linear combination model are very important to obtain high forecasting accuracy, SS-PSO is proposed and used to optimize the linear combination weights. In addition, the factors such as temperature and weekday are taken into consideration for short-term load forecasting. Simulation results show that accuracy of the load forecasting model proposed in the paper is higher than that of BP neural network, RBF neural network, support vector machine, EMD and their combinations.

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Acknowledgments

This study was partly supported by National Nature Science Foundation of China (U1134205, 51007074), Program for New Century Excellent Talents in University (NECT-08-0825) and Fundamental Research Funds for Central Universities (SWJTU11CX141) in China.

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Zhigang Liu, Wanlu Sun & Jiajun Zeng

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  1. Zhigang Liu
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  2. Wanlu Sun
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  3. Jiajun Zeng
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Correspondence to Zhigang Liu.

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Liu, Z., Sun, W. & Zeng, J. A new short-term load forecasting method of power system based on EEMD and SS-PSO. Neural Comput & Applic 24, 973–983 (2014). https://doi.org/10.1007/s00521-012-1323-5

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  • DOI: https://doi.org/10.1007/s00521-012-1323-5

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