Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems
Abstract
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.
Cite
@article{arxiv.2407.03368,
title = {Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems},
author = {Evgenii Genov and Julian Ruddick and Christoph Bergmeir and Majid Vafaeipour and Thierry Coosemans and Salvador Garcia and Maarten Messagie},
journal= {arXiv preprint arXiv:2407.03368},
year = {2025}
}
Comments
34 pages, contains the Appendix with a comment on KPIs, MPC formulation, Theoretical analysis of the MPC performance bounds and extra results on the in-sample performance