MATEC Web of Conferences
Volume 25, 20152015 International Conference on Energy, Materials and Manufacturing Engineering (EMME 2015)
|Number of page(s)||6|
|Published online||06 October 2015|
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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