A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

Abstract

Wind speed forecasting is an essential procedure in electric grid dispatching. Short-term wind speed forecasting can be a great challenge and an intractable issue in increasing wind energy output and guaranteeing power safety. The current wind speed forecasting models are based on a single model, which is generally an artificial intelligence model or a statistical forecasting method. However, these models cannot perform well in all cases. An effective combined model is proposed in this paper, and this model includes four parts: weight coefficient optimization calculation based on the nonpositive constraint combination theory, singular-spectrum analysis, combined forecasting and discussion of results. The developed model can decrease the negative influences of the component models and maximize the advantages of each component model. To evaluate the forecasting accuracy of our proposed model, ten minutes of wind speed data from Shandong Peninsula, China, were used as test cases. It is clearly demonstrated that the developed combined strategy outperforms the individual forecasting methods in terms of forecast performance and stability.

Keywords

• Combined model
• data preprocessing approach
• nonpositive constraint combination theory
• weight coefficient optimization