基于CEEMDAN-TCN的短期风电功率预测研究OA北大核心

Wind power generation,as an important component of renewable energy,plays a crucial role in power system planning and daily operation.Therefore,accurate short-term wind power forecasting is crucial for the stable operation and optimized scheduling of electrical grids.In order to enhance the precision of short-term wind power forecasting,a method of short-term wind power forecasting based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and temporal convolutional networks(TCN)is proposed.The CEEMDAN is used to decompose the initial wind power data,so as to obtain multiple several relatively stable sub-data sequences.The sub-data sequences are used as inputs for TCN,and the TCN model is used to conduct the feature extraction and power forecasting.All predicted values are aggregated to obtain the final power prediction value.The proposed method is verified by the real wind power data from a certain region in Ningxia,and compared with traditional prediction models.The results indicate that the proposed method has high prediction accuracy and can provide relevant references for short-term wind power forecasting and other related work.