Short-Term Wind Forecasting in Adrar, Algeria, Using a Combined System

[摘要] Forecasting wind speed using only one single model may not be satisfactory; however, the combination of several models may provide better results, especially in case of doubt about the best estimator (predicator). In this paper, we propose a short-term forecasting system in order predict the hourly averaged wind speed (HAWS) in the region of Adrar, Algeria. The proposed system is based on adaptive combination of two different models. The first model is an ARMA-based model, while the second model is an artificial neural network ANN-based model. To allow adaptive combination, models are associated to time varying coefficients that are updated recursively using the recursive least square algorithm (RLS). Numerical simulations have shown that for few hours in advance, the prediction error of the combined system is lower or at least equal to the best estimator.

[关键词] autoregressive moving average process;artificial neural networks;recursive least square method;wind speed forecasting;combined models