基 于 纵 向 联 邦 学 习 的 短 期 风 电 功 率 协 同 预 测 方 法

Because of the spatio-temporal correlation of wind resources, the forecasting accuracy of the forecasted wind farm can be improved by using the relevant data of adjacent wind farms. However, different wind farms are often owned by different power generation groups, which do not have access to private data of each other due to commercial competition and data security concerns. To solve these problems,first, a ridge regression forecasting model based on the improved k-nearest neighbor algorithm is proposed in this paper. Then, under the mechanism of vertical federated learning, the synchronous gradient descent algorithm is used to solve the proposed forecasting model iteratively. The distributed training process and the distributed forecasting process of wind power forecasting model are derived by utilizing the separable characteristic of gradient vector calculation. The original large-scale forecasting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. On the basis of ensuring the data privacy and security of all participants, the data information of adjacent wind farms can be effectively used to improve the accuracy of wind power forecasting. Finally, the results of case studies are given to demonstrate the effectiveness of the proposed method.