A short-term wind speed prediction method based on interval type 2 fuzzy model considering the selection of important input variables

Wind speed prediction is a challenging task in wind energy resource prediction. Wind speed time series, which are a highly variable data source, require highly nonlinear time characteristics for prediction. Most prediction models use hybrid models to improve prediction accuracy, which increases the complexity of the model. This paper proposes an interval type-2 (IT2) wind speed prediction model based on the selection of important input variables. First, the two-stage fuzzy curve are used to determine the model input so as to reduce redundant input and further reduce the complexity of the model. Second, a Gaussian membership function with a variable centre is used to further improve the prediction accuracy of the model. Finally, the recursive least squares method is used to complete the identification of the conclusion parameters. The proposed model has high generalisability and IT2 robustness, and can thus realise deterministic wind speed prediction. The wind speed prediction results for a wind farm in Colorado, USA, in two time periods show that compared with the type-1 fuzzy model and other type-2 fuzzy models, the proposed model provides more accurate short-term wind speed forecasting.