A New Wind Power Forecasting Approach Based on Conjugated Gradient Neural Network

Abstract

Prediction of the output power of wind plants is of great significance for running a power system comprising large amount of wind generators. According to the prediction results, it is possible to determine the quotas of power generation in power generators and distribute resources in a scientific and reasonable way. In the past, the Grey Neural Network was widely applied in predicting wind power while it could hardly meet the engineering requirements due to the structure of ANN. The problem of slow convergence speed and large amount of iterations, especially in case of large scale data, would pose challenges to power prediction and the sensitivity of automatic control. This paper optimizes ANN model by applying conjugate gradient descent and creating Conjugated Gradient Neural Network (CGNN) in weights updating process. Experiments performed on different scale datasets have proved that the performance of CGNN improves substantially as the average iterations decreased by almost 90% without the sacrifice of prediction accuracy.