Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving - Hong Liu; Zijun Zhang (Energy and AI)
This paper studies the renewable power forecasting task with a more advanced formulation, the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant. To realize such a task, an advanced domain-invariant feature learning embedded federated learning (DIFL) framework is proposed to coordinate the development of a system of deep network-based models serving as multiple clients and one server. In DIFL, each client, which serves each local renewable power plant, maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model. The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator. Therefore, only desensitized data, such as parameters of the models, are allowed to be transmitted among end users for preserving local data privacy of power plants. To verify the advantages of the DIFL, a preliminary exploration of its theoretical property is first conducted. Next, computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants. Results further confirm that, in terms of the averaged performance, the DIFL consistently realizes improvements against all benchmarks based on both real wind farm and solar power plant datasets.