Using a Model Structure Selection Technique to Forecast Short-Term Wind Speed for a Wind Power Plant in North China

摘要

Model structure selection with respect to short-term wind speed forecasting is relatively difficult due to the stochastic and intermittent nature of the wind speed distribution. In order to overcome the disadvantages in traditional approaches such as computing burden and low accuracy, a novel model structure selection technique about short-term wind speed forecasting is proposed in order to improve the computational efficiency and forecasting accuracy using the model variable selection, variable order estimation, model structure optimization techniques, and so on. The detailed and complete process flow associated to the theoretical analysis of the proposed model structure selection technique is described. Moreover, both the so-called overkill in the data filtering and so-called overfitting in the learning processing are avoided by a proper technique in the design of proposed approach. In order to verify the effectiveness of proposed strategy in a practical application, all the experimental results are evaluated based on the real data provided by a sampling device with respect to a low-wind-speed wind turbine (i.e.,FD-77) of a wind power plant of north China. Finally, the developed model structure selection technique is verified by the cross-validation method.