期刊
IEEE TRANSACTIONS ON SMART GRID
卷 13, 期 1, 页码 268-279出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3115904
关键词
Solar power generation; Load modeling; Data models; Collaborative work; Probabilistic logic; Training; Meteorology; Behind-the-meter solar photovoltaic; energy disaggregation; privacy preservation; federated learning; Bayesian theory
资金
- Australia-China Science and Research Fund Joint Research Center for Energy Informatics and Demand Response Technologies
- Australian Research Council (ARC) [DP200103494]
- Australian Research Council [DP200103494] Funding Source: Australian Research Council
Accurate estimation of residential solar photovoltaic generation is crucial for power distribution and demand response programs. A novel method using a federated learning-based Bayesian neural network (FL-BNN) is proposed to disaggregate BTM solar generation at the community level, preserving utility privacy. The effectiveness of the method is validated on a publicly available dataset.
Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisible to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner assuming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM solar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to capture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant features can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.
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