Probabilistic Forecast of Wind Power Generation with Data Processing and Numerical Weather Predictions

In recent years, with the increasing proportion of renewable energy, some system problems have gradually emerged. To reduce the economic cost of system operations and improve power system reliability, renewable power forecasting is an indispensable part. Compared with the deterministic prediction, the probabilistic forecast considers the uncertainty, which helps manage risks and make decisions for power grids. This study proposes a novel probabilistic forecasting method for wind power generation, which includes data preprocessing, adaptive neuro fuzzy inference system (ANFIS) training model with fuzzy c-means clustering algorithm, and post processing of predicted-interval. The input data of the proposed probabilistic forecasting model include the numerical weather prediction (NWP) ensemble wind speeds, NWP spot wind-speed forecasts, and historical wind power measurements. For practical applications, measured data of power generation at actual wind farms were used to compare different forecasting models. The research results demonstrate that the proposed model supports better performance and prediction stability. Furthermore, this work reveals the importance of both data preprocessing and post processing of predicted interval on wind power forecasting. These essential processes greatly improve the performance of the probabilistic wind power forecasts.