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

• URL: http://arxiv.org/abs/2407.03368v5
• Date: Tue, 15 Apr 2025 15:12:35 GMT
• Title: Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems
• Authors: Evgenii Genov, Julian Ruddick, Christoph Bergmeir, Majid Vafaeipour, Thierry Coosemans, Salvador Garcia, Maarten Messagie,
• Abstract summary: This study investigates the integration of forecasting and optimization in energy management systems.<n>We develop a theoretical and empirical framework to examine how forecast accuracy and stability interact with switching costs.
• Score: 3.3295510777293837
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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