Secondary factor induced wind speed time-series prediction using self-adaptive interval type-2 fuzzy sets with error correction

Accurate wind speed forecasting is very crucial for wind power generation systems, but the inherent randomness of wind speeds makes wind speed forecasting challenging. There have been many studies on predicting wind speeds, but they ignored the influence factor of wind speed on its change over time with multiple factors, such as wind direction, temperature, humidity and atmospheric pressure. Therefore, a secondary factor induced wind speed time series prediction using self-adaptive interval type-2 fuzzy sets (IT2FS) with error correction was proposed. First, an IT2FS model is developed to induce secondary factors to predict wind speed. Specifically, the differential evolution algorithm is employed to optimize parameters of IT2FS model. Second, error correction strategy is adopted to correct the model error. The auto-regressive integrated moving average model is used to predict the residual sub-sequence after variational mode decomposition. Finally, by predicting the wind speed of two wind farms in China, it is verified that the proposed hybrid system transcends other compared models and simultaneously realizes high accuracy and strong stability. Thus, employing a new strategy to conduct the main factor time series prediction using its secondary factors is extremely useful for enhancing the prediction performance.