Abstract
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.
Highlights
Power load forecasting is a predictive mathematical model based on the characteristics of the power load, integration of economics and social and meteorological data to predict and prepare for future power load changes.According to the time this model forecasted, the power load forecasting can be categorized as long-term, medium-term, short-term and super short-term power load forecasting
This paper proposed a 24 hours power load forecasting model using support vector machine (SVM) optimized by wavelet transform and particle swarm algorithm, avoiding the shortcoming of artificial neural network method
The prediction of low frequency series data can be done with SVM; the prediction of high frequency series data can be done with strong randomness; the weighted average method will be applied [6]
Summary
Power load forecasting is a predictive mathematical model based on the characteristics of the power load, integration of economics and social and meteorological data to predict and prepare for future power load changes. This paper proposed a 24 hours power load forecasting model using support vector machine (SVM) optimized by wavelet transform and particle swarm algorithm, avoiding the shortcoming of artificial neural network method. If the training data is linear and integrable, the linear boundary of this training data is w, x Rn u R wT x + b = 0 , wT x + b t 1( x A) and wT x + b d1 x B, which can be shown as yi[(w7 x xi ) + b 1] t 0 ; the decision function is fw,b (x) sign(wT x b) , w is the weighed vector, and b is deviation This question can be transformed into solving the quadratic linear optimization equation:. This paper uses Gauss radial basis function [10]
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