Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting

Abstract

Wind speed forecasting is essential for the dispatch, controllability, and stability of power grids, and its accuracy is vital to the effective use of wind resources. In this study, a novel hybrid wind speed forecasting system is developed based on the data area division (DAD) method and a deep learning neural network model. The system consists of three modules: extraction module, data preprocessing module, and forecasting module. In the data extraction module, a large amount of valid historical data is extracted, filtered, and classified from the forecast location and the surrounding locations. In the data preprocessing module, complementary ensemble empirical mode decomposition is used to decompose the wind speed data. In the forecasting module, a long short-term memory network optimized by using a genetic algorithm is used to forecast the decomposed wind speed data and integrate them into the final forecast results. Numerical simulation results show that (a) the forecast system maintains RMSE in the range of 0.2–0.6 m/s and MAPE in the range of 3.0–7.0% for short-term wind speed forecasts at different locations for different time periods, showing good stability. (b) For wind speed forecasting at different time intervals, the accuracy of wind speed forecasting at 10-minute and 30-minute intervals is better, while the accuracy of forecasting at a 60-minute interval needs to be improved, but overall, the forecasting system shows good generalizability. (c) The forecast system improves the forecast accuracy of short-term wind speed forecasting more effectively than other conventional methods, and the improvement of RMSE and MAPE remains in the range of 14–39% and 13–27% even compared with the hybrid forecast model that has better forecast accuracy. (d) For area-wide short-term wind power forecasting, the forecast deviation value of this forecasting system remains below 6% throughout the year, showing good practicality.