A Wind Speed Interval Prediction Framework Based on Machine Learning Models and Kernel Density Estimation Method

Predicting wind speed is crucial in the wind energy sector, especially for controlling and balancing production and consumption in smart grids. However, wind speed is characterized by high uncertainty and variability. These fluctuations in wind speed are obvious in short term timescales. Deterministic wind speed forecasts, also named point predictions, do not consider the inherent uncertainties in wind speed predictions. Estimating these uncertainties is essential for providing reliable information to energy operators, enabling them to develop effective operational strategies. In this work, we introduce a probabilistic prediction intervals approach for short-term wind speed forecasting. The proposed framework consists of a hybrid model that integrates machine learning models and an automatic feature selection technique to estimate both point predictions and prediction intervals for wind speed. The simulation works demonstrate the effectiveness of our designed framework and demonstrate its ability to generate satisfactory prediction intervals in the most adopted evaluation criteria.