Short-term Load Forecasting Of Power System Based On Wavelet And Neural Network

Short-term load forecasting of power system is a very important job, precision forecasting requires comprehensive considerations and excellent prediction models and prediction methods to be guaranteed. In recent years, many experts and scholars made a lot of intelligent algorithm is applied to the power system load forecasting instance, there are several intelligent algorithms used in conjunction with the case, making the power system load forecasting has been rapid development, the prediction accuracy is also a great increased. This article introduces wavelet analysis theory in power system load forecasting using neural networks to predict constructed wavelet and neural network short-term load forecasting model.Wavelet analysis theory is a relatively new concept, in recent years, slowly apply the relevant aspects of the power system using wavelet analysis theory to decompose the original load sequence, rather than directly using neural network load forecasting, which can be load data series high frequency components and low frequency components of unbundled, better load data sequence mining more deep-seated hidden information, and then use the neural network algorithm for each prediction, we use the BP neural network, RBF neural network and GRNN neural networks to predict, but also very classic gray model GM (1,1) is introduced into the model, the final sequences were then reconstructed wavelet decomposition, you can get predictable results. The model is applied to a specific example, the results show that the prediction accuracy fully meet the requirements, and over not using wavelet analysis theory model of prediction accuracy has improved to some extent.