基于EEMD-SE和PSO-KELM的短期负荷区间预测方法

A Short-Term Load Interval Forecasting Method Based on EEMD-SE and PSO-KELM

准确的短期负荷预测在电力系统中发挥着至关重要的作用。近年来,大量短期负荷预测研究表明,与点预测相比,负荷的区间预测可以更有效地保证电力系统的安全运行。因此,提出一种基于EEMDSE和PSO-KELM的短期负荷区间预测方法。首先,使用集合经验模态分解(EEMD)将原始负荷序列分解为一系列的子序列;然后,通过样本熵(SE)对各序列进行计算,量化序列的复杂程度,将SE值较小的序列进行重构;最后,通过粒子群(PSO)优化核极限学习机(KELM)的输出层权重,建立预测模型,并对各序列进行区间构造。采用南方某市不同季节的实际负荷数据对所提模型进行实验验证,仿真结果表明,与其他预测方法相比,所提方法在区间可靠性以及宽度上具有更好的效果。

Accurate load forecasting plays an important role in power system.In recent years,a large number of load forecasting studies show that compared with point forecasting,interval forecasting of load can effectively ensure the safe operation of power system.This paper presents a short-term load interval forecasting method based on EEMD-SE and PSO-KELM.Firstly,the ensemble empirical mode decomposition(EEMD)is used to decompose the original load series into a series of subseries.Then,the sample entropy(SE)is used to calculate and quantify the complexity of the series,and the series with small SE values are reconstructed.Finally,the particle swarm optimization(PSO)is used to optimize the weight of output layer of kernel extreme learning machine(KELM),and a prediction model is established to reconstruct the interval of each subseries.The proposed model was tested with the actual load data of a city in South China in different seasons under different nominal confidence,and the simulation results show that compared with other prediction methods,the proposed method has better performance in interval reliability and width.