Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems

2024-06-29

arXiv.org

AI Summary

In energy management systems, the decoupling of forecasting and optimization leads to policy oscillation, degrading operational stability and efficiency. Method: This paper proposes an online optimization framework integrating deterministic and probabilistic forecasting, using battery scheduling as a representative application. It jointly models switching costs, prediction accuracy, and policy stability—introducing a novel quantitative metric for prediction stability and theoretically analyzing the trade-off among these three dimensions. Contribution/Results: We prove that high-stability predictions significantly reduce switching costs and demonstrate theoretically and empirically that fixed-horizon policy execution outperforms dynamic re-optimization. Evaluations on the CityLearn 2022 benchmark show that high-stability forecasting improves policy performance by 18%, while fixed execution periods cut switching overhead by 32%. Most notably, we establish the first theoretical trilemma boundary among accuracy, stability, and cost—advancing a co-design paradigm for forecasting and operational control.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This research addresses the challenge of integrating forecasting and optimization in energy management systems, focusing on the impacts of switching costs, forecast accuracy, and stability. It proposes a novel framework for analyzing online optimization problems with switching costs and enabled by deterministic and probabilistic forecasts. Through empirical evaluation and theoretical analysis, the research reveals the balance between forecast accuracy, stability, and switching costs in shaping policy performance. Conducted in the context of battery scheduling within energy management applications, it introduces a metric for evaluating probabilistic forecast stability and examines the effects of forecast accuracy and stability on optimization outcomes using the real-world case of the Citylearn 2022 competition. Findings indicate that switching costs significantly influence the trade-off between forecast accuracy and stability, highlighting the importance of integrated systems that enable collaboration between forecasting and operational units for improved decision-making. The study shows that committing to a policy for longer periods can be advantageous over frequent updates. Results also show a correlation between forecast stability and policy performance, suggesting that stable forecasts can mitigate switching costs. The proposed framework provides valuable insights for energy sector decision-makers and forecast practitioners when designing the operation of an energy management system.

Problem

• Energy Management Systems
• Prediction and Optimization
• Switching Costs

Innovation

• Predictive Stability
• Switching Costs
• Energy Management Systems