Privacy-preserving Decision-focused Learning for Multi-energy Systems

TL;DR

This paper introduces a privacy-preserving decision-focused learning framework for multi-energy systems, enabling secure, cost-effective dispatch decisions without compromising sensitive load data.

Contribution

It proposes novel privacy-preserving techniques, including information masking, matrix decomposition, and homomorphic encryption, tailored for decision-focused learning in multi-energy systems.

Findings

Achieves lower average daily dispatch costs compared to existing methods.

Effectively protects sensitive load data during model training.

Demonstrates robustness and security through theoretical analysis and real-world case studies.

Abstract

Decision-making for multi-energy system (MES) dispatch depends on accurate load forecasting. Traditionally, load forecasting and decision-making for MES are implemented separately. Forecasting models are typically trained to minimize forecasting errors, overlooking their impact on downstream decision-making. To address this, decision-focused learning (DFL) has been studied to minimize decision-making costs instead. However, practical adoption of DFL in MES faces significant challenges: the process requires sharing sensitive load data and model parameters across multiple sectors, raising serious privacy issues. To this end, we propose a privacy-preserving DFL framework tailored for MES. Our approach introduces information masking to safeguard private data while enabling recovery of decision variables and gradients required for model training. To further enhance security for DFL, we design a safety protocol combining matrix decomposition and homomorphic encryption, effectively preventing collusion and unauthorized data access. Additionally, we developed a privacy-preserving load pattern recognition algorithm, enabling the training of specialized DFL models for heterogeneous load patterns. Theoretical analysis and comprehensive case studies, including real-world MES data, demonstrate that our framework not only protects privacy but also consistently achieves lower average daily dispatch costs compared to existing methods.