# Decision-focused learning for optimal PV-Battery scheduling

arXiv: https://arxiv.org/abs/2605.28340 · HTML: https://mlsanity.com/paper/2605.28340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 9 of 9)

- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Optimization Learning](https://mlsanity.com/paper/2501.03443/) (2025)
- [LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch](https://mlsanity.com/paper/2008.07025/) (2020)
- [Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach](https://mlsanity.com/paper/2309.00803/) (2023)
- [Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations](https://mlsanity.com/paper/2206.10795/) (2022)
- [Predict+Optimize Problem in Renewable Energy Scheduling](https://mlsanity.com/paper/2212.10723/) (2022)
- [SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe](https://mlsanity.com/paper/2405.14472/) (2024)
- [Convex Optimization of PV-Battery System Sizing and Operation with Non-Linear Loss Models](https://mlsanity.com/paper/2307.15507/) (2023)

## Abstract (arXiv)

The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation of a photovoltaic-battery system can bring significant savings to households. Optimal control requires correct forecasts of underlying parameters, such as photovoltaic power generation, to schedule the battery. While forecasting models have become increasingly accurate due to algorithmic advances and data availability, accuracy is typically measured in generic metrics which might not align with the downstream application. This study proposes a decision-focused learning framework that integrates optimization and prediction by training a Long Short-Term Memory photovoltaic energy forecaster on the downstream optimal scheduling of a battery system. The proposed methodology is compared against a standard two-phase approach. Across a 14-month evaluation period, the decision-focused method reduced average electricity costs across twenty buildings by 3.6% when normalized against performance bounds defined by a perfect forecast and a baseline of no optimization. Critically, this financial improvement was achieved despite the model exhibiting a root mean squared error of 19.9%, significantly higher than the decoupled model's 8.2%. Warm-starting the decision-focused model further improves results, lowering average cost by approximately 8%, while also mitigating the negative impact on statistical accuracy (root mean squared error of 13.7%). The findings are statistically significant at the 0.001 level across the twenty households and for each household individually. These results demonstrate that aligning forecast models with optimization goals is key for achieving cost advantages in PV-battery systems. Future research should replicate these findings on other datasets, alternate forecasting models and alternate optimization algorithms.