Short-term wind power forecasting model based on random forest algorithm and TCN
作者单位：School of Mechanical Electronic ＆ Information EngineeringChina University of Mining ＆ Technology （Beijing）
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Random forest Feature Selection Temporal convolution networks(TCN) Wind power forecast
摘 要：Wind power has strong stochastic volatility, short-term forecasting accuracy is not high. With the increase of grid connected capacity of wind farms, the overall prediction error has a greater impact on the system operation. This paper investigates the short-term power prediction problem for wind farms based on historical wind power and meteorological ***, the preprocessed original data are divided into training set and test set, and the random forest（RF） algorithm is applied to screen out the feature data that have the greatest impact on the output power from all the feature data of the training set. Then,the training and the test sample sets are reconstructed based on the screening results, and the temporal convolution network（TCN）is built by finding the optimal hyper-parameter combination of the network with the grid search algorithm. Finally, comparative experiments for a wind farm in a certain area of North China are presented to demonstrate the effectiveness and advancement of the proposed prediction model.