Short-term wind speed interval prediction by improved regularized extreme learning machine based on attribute reduction

Abstract

Accurate wind speed forecasting is important for stable operation of the power system when large-scale wind power is connected to the grid. According to the randomness of wind speed caused by the interaction of weather attributes, this article presents a new wind speed interval prediction method by improved regularized extreme learning machine based on attribute reduction. First, the principal component analysis is used to extract the principal component score sequences of multi-dimensional meteorological attribute factors, and the principal component score sequences are weighted by the variance contribution rate. Then, the original wind speed series is processed by fuzzy information granulation to obtain three components, which represent the minimum value, maximum value, and variation trend of the wind speed interval. The weighted principal component score sequence and the wind speed fuzzy granulation component are used as the input model of the prediction model, and the gradient prediction is performed using the improved regularized extreme learning machine of the gravity search algorithm. Finally, the prediction effect of the proposed method is simulated and analyzed based on the measured data of the wind farm. The results show that the combination prediction method can effectively improve the operational efficiency and accuracy of wind speed prediction, and has strong generalization ability.