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

arXiv: https://arxiv.org/abs/2510.21858 · HTML: https://mlsanity.com/paper/2510.21858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [MIPaaL: Mixed Integer Program as a Layer](https://mlsanity.com/paper/1907.05912/) (2019)
- [LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch](https://mlsanity.com/paper/2008.07025/) (2020)
- [Cost-Oriented Load Forecasting](https://mlsanity.com/paper/2107.01861/) (2021)
- [Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach](https://mlsanity.com/paper/2305.00362/) (2023)
- [Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach](https://mlsanity.com/paper/2309.00803/) (2023)
- [Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage](https://mlsanity.com/paper/2406.17085/) (2024)
- [Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading](https://mlsanity.com/paper/2209.12025/) (2022)
- [Load Data Valuation in Multi-Energy Systems: An End-to-End Approach](https://mlsanity.com/paper/2311.09839/) (2023)
- [A Scenario-based Branch-and-Bound Approach for MES Scheduling in Urban Buildings](https://mlsanity.com/paper/2003.03750/) (2020)

## Abstract (arXiv)

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.