Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

2025-04-25

AI Summary

To address the net load decomposition challenge arising from unobservable behind-the-meter distributed photovoltaic (PV) generation, this paper proposes a privacy-preserving personalized federated learning framework. To tackle cross-regional statistical heterogeneity and strict data privacy constraints, the framework introduces a novel two-tier personalization strategy: at the local level, a Transformer architecture extracts temporal irradiance features, augmented with an adaptive local aggregation mechanism to mitigate user behavioral and geographical disparities; at the global level, differential privacy–guaranteed inter-center knowledge sharing enhances model generalization. Evaluated on a real-world multi-region PV dataset, the method reduces decomposition error by 18.7% over baseline approaches, demonstrating significantly improved robustness to both geographical and behavioral heterogeneity. This work establishes a new paradigm for high-accuracy, privacy-sensitive PV generation inversion in distributed energy systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance

Abstract

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods.

Problem

• Estimating unobservable PV generation from net load
• Addressing statistical heterogeneity in federated learning
• Preserving privacy in distributed PV disaggregation

Innovation

• Personalized Federated Learning for PV disaggregation
• Transformer-based model for solar irradiance embeddings
• Adaptive local aggregation to reduce heterogeneity impact