Data exchange between multiple renewable energy power plant owners can lead to an improvement in forecast skill thanks to the spatio-temporal dependencies in time series data. However, owing to business competitive factors, these different owners might be unwilling to share their data. In order to tackle this privacy issue, this paper formulates a novel privacy-preserving framework that combines data transformation techniques with the alternating direction method of multipliers. This approach allows not only to estimate the model in a distributed fashion but also to protect data privacy, coefficients and covariance matrix. Besides, asynchronous communication between peers is addressed in the model fitting, and two different collaborative schemes are considered: centralized and peer-to-peer. The results for a solar energy dataset show that the proposed method is robust to privacy breaches and communication failures, and delivers a forecast skill comparable to a model without privacy protection.

中文翻译：

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用于可再生能源预测的隐私保护分布式学习

由于时间序列数据的时空依赖性，多个可再生能源发电厂所有者之间的数据交换可以提高预测技能。然而，由于业务竞争因素，这些不同的所有者可能不愿意共享他们的数据。为了解决这个隐私问题，本文制定了一种新颖的隐私保护框架，该框架将数据转换技术与乘法器的交替方向方法相结合。这种方法不仅允许以分布式方式估计模型，而且还可以保护数据隐私、系数和协方差矩阵。此外，模型拟合中解决了对等点之间的异步通信，并考虑了两种不同的协作方案：集中式和点对点。