An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China

Wind speed forecasting plays a crucial role in enhancing the operating efficiency of wind power systems for generating electric power. Currently, a substantial number of approaches have been developed to improve the precision of wind speed forecasting. However, owing to the instability and fluctuation of wind speed, many models ignore the deficiencies of the individual models and data preprocessing strategies, which leads to results with poor accuracy. In this study, a novel forecasting system that combines data denoising methods, traditional forecasting algorithms, and a combination optimization approach to predict wind speed is proposed. To analyze the training and testing dataset, this study uses the 10-min original wind speed dataset from a wind farm in Penglai, China. Based on the results of three comparative numerical simulations and the discussion of the proposed forecasting system, it is revealed that the developed model performs more effectively than other models. Therefore, in this study we conclude that the proposed combined forecasting system is an efficient and promising technique that provides precise results for predicting wind speed in the short term, and it could be employed for further applications in energy systems.