论文信息 - A PSO-SVM-based 24 Hours Power Load Forecasting Model

A PSO-SVM-based 24 Hours Power Load Forecasting Model

In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine) power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.

Xiaoxu Yu | Haisen Ji

[1] Fan Yina. The short-term load foreacasting based on wavelet transform , 2012 .

[2] Xiao-Bo He,et al. Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm , 2017 .