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

TL;DR

This paper explores how forecast accuracy, stability, and switching costs interact in energy management, proposing a framework that balances these factors to improve decision-making in real-world battery scheduling.

Contribution

It introduces a theoretical and empirical framework analyzing forecast accuracy and stability trade-offs considering switching costs in energy systems.

Findings

Switching costs significantly impact forecast accuracy and stability trade-offs.

More stable forecasts can reduce performance loss from switching.

Longer commitment periods may outperform frequent switching under high switching costs.

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.