Multivariate federated tree-based forecasting combining resilience and privacy: Application to distributed energy resources

Abstract

Using spatiotemporal data can increase forecasting accuracy for distributed energy resources such as wind farms, solar power plants, and electric vehicle charging stations. The underlying forecasting models assume that data are shared by the different data owners, which is not always feasible because of privacy and confidentiality constraints. Existing methods for privacy-preserving data-sharing are based mainly on linear or non-linear univariate models. This is quite limiting concerning the potential of non-linear multivariate forecasting approaches, since these models can be computationally more efficient than univariate approaches and can learn non-linear relationships. This paper introduces a new federated forecasting approach implementing a multivariate non-linear learning scheme based on decision trees while enabling fully decentralized computation. Results show higher predictive performance than existing linear or univariate energy forecasting models while preserving privacy.