Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate I day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.