国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to improve the accuracy of short-term prediction of wind power, in view of the shortcomings of KNN (K-Nearest Neighbor algorithm) algorithm in wind power prediction, a short-term wind power forecasting method based on K-means and an improved KNN algorithm is proposed . The K-means clustering method is used to determine the types of historical wind power samples, the method of searching for similar historical sample sets in the KNN algorithm is improved and optimized, a prediction model is constructed, and the C/S architecture is used to realize the design of the prediction system. The system has a self-correction function, which can continuously correct the forecast model as the number of forecasts increases, and gradually reduce the error rate of the forecast. A simulation analysis with historical data of a wind farm in Jilin Province is carried out. The results show that compared with other algorithms, the algorithm has the largest decrease in average absolute error and root mean square error by 1.08% and 0.48%, and the calculation time has increased by 5.45%,ultra-short-term multi-step forecasting has the value of promotion and application.