Multi-Step-Ahead Combination Forecasting of Wind Speed Using Artificial Neural Networks

Wind speed plays a very important role in the scheduling of power systems and dynamic control of wind turbine. Wind speed forecasting has become one of the most important issue for wind energy conversion recently. Adaptive and reliable methods and techniques for wind speed forecasting are urgently needed in view of its stochastic nature that varies from time to time and from site to site. Back Propagation (BP) algorithm-based neural network, which is a commonly computational intelligence method, has been widely used in forecasting fields. But it does have some deficiencies and uncertainties, for example, the hidden nodes of BP directly affect the network’s generalization ability and accuracy, but there is not yet an effective theory to determine the number of hidden nodes. In order to solve the problem of BP network, a combination forecasting model with differently weighed BP networks is proposed in this study. Wind speed data collected from a New Zealand wind power plant is used for experiment research. Simulations show that the results of combination forecasting method is better than those of only one BP network.