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

机译：基于EEMD和SS-PSO的电力系统短期负荷预测新方法

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摘要

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 (SSPSO) 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.
机译：针对基于经验模式分解（EMD）的短期负荷预测的缺点，如模式混合和许多高频随机分量，基于整体经验模式分解（EEMD）和分段的新短期负荷预测模型本文提出了粒子群优化算法（SSPSO）。首先，将负载序列分解为有限数量的本征模函数（IMF）分量，并通过EEMD将其分解为一个余数，从而避免了传统EMD的模式混合问题。然后，通过计算和观察分解序列的频谱，提取并重构了一些低频IMF。可以使用适当的预测模型来预测其他IMF。由于IMF1是负荷序列的主要随机分量，因此采用线性组合模型来预测IMF1。由于线性组合模型的权重对于获得较高的预测精度非常重要，因此提出了SS-PSO并用于优化线性组合权重。此外，在短期负荷预测中会考虑温度和工作日等因素。仿真结果表明，本文提出的负荷预测模型的准确性高于BP神经网络，RBF神经网络，支持向量机，EMD及其组合。

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