Short-term Electricity Price Prediction of Smart Grid Based on IGWO-KELM
Establishing an accurate electricity price prediction model in the mass data is of great significance for enterprises and power users to make reasonable decisions.In view of the large amount of data affecting the electricity price prediction model,principal component analysis(PCA)method is adopted to extract the main features and reduce the data dimension.In order to improve the accuracy of electricity price prediction,the good point set initialization population and the hyperbolic convergence factor method are proposed by considering that the random initialization and linear convergence factor of traditional wolf pack algorithm affect the convergence speed and accuracy.In order to improve the stability and generalization ability of the kernel ex-treme learning machine,the regularization coefficient C and kernel parameter g of the kernel extreme learning machine are opti-mized by the improved wolf pack algorithm.Simulation results show that the improved gray wolf algorithm has better conver-gence speed and accuracy.And the improved gray wolf optimized kernel extreme learning machine is more suitable for electrici-ty price prediction than the traditional algorithm.
gray wolf algorithmgood point setkernel extreme learning machineelectricity price prediction
万方数据
维普
