Error Evaluation of Short-Term Wind Power Forecasting Models

Inconsistency and randomness of wind power impose massive challenges to large-scale wind power production. An accurate production of the wind power for the upcoming hours is imperative, in order that accurate planning and scheduling of the wind power production from conventional units can be accomplished. In the present work, we have proposed three intelligent forecasting models using fuzzy logic, artificial neural network (ANN) and adaptive-neuro-fuzzy inference system (ANFIS) approaches. These models can efficiently incorporate the uncertainty and nonlinearity linked with climatic parameters. To implement these models, the forecasting has been done using historical data of various stations. The performance of these intelligent forecasting models are estimated with statistical indicators and observed that the results obtained using ANFIS forecasting model are found quite accurate. Consequently, ANFIS model can be useful for accurate forecasting of wind power and for efficiently utilizing the wind resources.