Novel deterministic and probabilistic combined system based on deep learning and self-improved optimization algorithm for wind speed forecasting

Abstract

Wind energy is a special type of renewable low-carbon energy with no greenhouse gas emissions. It can be used for power generation and grid stability improvement in the engineering field to provide highly accurate forecasts. However, most existing forecasting models ignore the role of data preprocessing and optimization methods, resulting in low forecasting accuracy. To fill this gap, a novel combined forecasting system that performs deterministic and probabilistic forecasts is proposed. The system has three parts: an advanced data denoising algorithm, deep learning forecasting models, and a self-improved multi-objective optimization algorithm for improving the performance of wind speed forecasting. The self-improved optimization algorithm can converge to global optimum based on theoretical proof. Using three datasets from 10-min wind speed data, several controlled experiments are designed and implemented to demonstrate the forecasting performance of the combined system in terms of deterministic and probabilistic forecasting. After the experiment, the forecasting effectiveness, improvement ratio of metrics, sensitivity, and computational complexity of this system are presented, further demonstrating the advantages of the combined system. Through the foregoing techniques, the proposed system resolves the problem of inaccurate wind speed forecasting, thus supplementing the existing field.