A PSO-SVM-based 24 Hours Power Load Forecasting Model
State Grid Liaoyang Electric Power Supply Company, Liaoyang, Liaoning, China
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.
Key words: wavelet transform / particle swarm optimization / support vector machine
© Owned by the authors, published by EDP Sciences, 2015
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