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

doi:10.11930/j.issn.1004-9649.202001103

Abstract

Abstract: 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.

Key words: load forecasting, ensemble empirical mode decomposition, kernel extreme learning machine, interval forecasting, load characteristics, parameter optimization

ZHANG Lin, LIU Jichun. A Short-Term Load Interval Forecasting Method Based on EEMD-SE and PSO-KELM[J]. Electric Power, 2021, 54(3): 132-140.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202001103

https://www.electricpower.com.cn/EN/Y2021/V54/I3/132

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