# Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems

arXiv: https://arxiv.org/abs/2407.03368 · HTML: https://mlsanity.com/paper/2407.03368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems](https://mlsanity.com/paper/1911.10092/) (2019)
- [Online Convex Optimization Using Predictions](https://mlsanity.com/paper/1504.06681/) (2015)
- [On Forecast Stability](https://mlsanity.com/paper/2310.17332/) (2023)
- [Optimal Network Charge for Peer-to-Peer Energy Trading: A Grid Perspective](https://mlsanity.com/paper/2205.01945/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data-driven solar forecasting enables near-optimal economic decisions](https://mlsanity.com/paper/2509.06925/) (2025)
- [Beyond Accuracy: A Stability-Aware Metric for Multi-Horizon Forecasting](https://mlsanity.com/paper/2601.10863/) (2026)

## Abstract (arXiv)

This study investigates the integration of forecasting and optimization in energy management systems, with a focus on the role of switching costs -- penalties incurred from frequent operational adjustments. We develop a theoretical and empirical framework to examine how forecast accuracy and stability interact with switching costs in online decision-making settings. Our analysis spans both deterministic and stochastic optimization approaches, using point and probabilistic forecasts. A novel metric for measuring temporal consistency in probabilistic forecasts is introduced, and the framework is validated in a real-world battery scheduling case based on the CityLearn 2022 challenge. Results show that switching costs significantly alter the trade-off between forecast accuracy and stability, and that more stable forecasts can reduce the performance loss due to switching. Contrary to common practice, the findings suggest that, under non-negligible switching costs, longer commitment periods may lead to better overall outcomes. These insights have practical implications for the design of intelligent, forecast-aware energy management systems.