A Bi-Party Engaged Modeling Framework for Renewable Power Predictions With Privacy-Preserving
Hong Liu, Zijun Zhang
Abstract
This paper presents a pioneering study in developing data-driven models for predicting the future renewable power output sequence via using numerical weather predictions of multiple sites without breaching the data privacy. A novel bi-party engaged data-driven modeling framework (BEDMF) is developed to enable efficiently learning local and global latent features serving as decentralized data for data-driven modeling with privacy-preserving. The BEDMF contains two stages, the pretraining stage and fine-tuning. At the pretraining stage of the BEDMF, local latent features are learned via local models and then aggregated to produce the global latent feature via a global model. At the fine-tuning stage, local latent features are learned using local data and global latent feature from the previous iteration. The proposed framework enables capturing spatial-temporal patterns among multiple sites to further benefit modeling in renewable power prediction tasks. Meanwhile, the framework preserves the data privacy via isolating data locally in the clients. To verify the advantage of the BEDMF, a comprehensive computational study is conducted to benchmark it against famous baselines. Results show that the BEDMF achieve at least 3% improvements on average.