【摘要】 风速预测精度的提高,对降低风力发电成本、合理安排风场选址等方面有着积极作用。使用DBSCAN聚类对所有数据进行去噪处理,选择最合适的风速数据序列进行实证研究。首先,针对风速数据序列具有混沌性而对预测结果产生影响的问题,采用C-C法确定相空间重构中所需参数。与此同时,结合混沌理论建立混沌支持向量机模型,用以预测未来24 h的风速值。之后,将该模型与EGARCH模型以及具有外生输入的非线性自回归网络(NARX)模型的预测结果进行对比。最后,根据各预测模型的RMSE和MAPE精度对模型预测效果进行评估。结果表明:基于混沌时间序列的支持向量机模型对NWTC m2气象站所在地风速具有最佳预测效果。更多还原

【Abstract】 The improvement of wind speed prediction accuracy plays a positive role in reducing the cost of wind power generation and arranging the location of wind farms. The DBSCAN clustering method is used to denoise all data and select the most appropriate sequences for empirical research. Firstly, since the chaotic wind speed data sequences would affect the prediction results, C-C method is made to determine the needed parameters in phase space reconstruction. Meanwhile, a model of chaos support vector machine combined chaos theory is established to predict the wind speed value in the coming 24 h. Then the model is compared with the EGARCH model and the nonlinear self-regression network with exogenous input(NARX) model in terms of prediction results. Finally, the prediction effect of models is evaluated by the RMSE and MAPE of each prediction model. The results show that the support vector machine model based on the chaotic time series has the best prediction effect on the wind speed of the NWTC m2 weather station.更多还原