用扩散模型提升预测不确定性，优化储能系统决策
Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control
- 将扩散模型用于系统状态预测，生成未来概率轨迹
- 在纽约州日前电力市场中，收益比传统方法高12.3%
- 适合需要应对不确定性的动态控制系统设计
我们提出Diffusion-Informed Model Predictive Control（D-I MPC），一种通用框架，通过将基于扩散的时间序列预测模型融入模型预测控制算法，实现对部分可观测随机系统的不确定性感知预测与决策。该方法利用扩散模型对系统随机分量的演化进行概率估计，并将其融入MPC算法，以在不确定性下优化未来轨迹与动作选择。我们在纽约州日前电力市场中的电池储能系统能量套利任务上进行了评估。实验结果表明，基于扩散预测器的模型方法显著优于采用经典预测方法和无模型强化学习基线的实现。
原文摘要 · Abstract (English)
We propose Diffusion-Informed Model Predictive Control (D-I MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable stochastic systems by integrating diffusion-based time series forecasting models in Model Predictive Control algorithms. In our approach, a diffusion-based time series forecasting model is used to probabilistically estimate the evolution of the system's stochastic components. These forecasts are then incorporated into MPC algorithms to estimate future trajectories and optimize action selection under the uncertainty of the future. We evaluate the framework on the task of energy arbitrage, where a Battery Energy Storage System participates in the day-ahead electricity market of the New York state. Experimental results indicate that our model-based approach with a diffusion-based forecaster significantly outperforms both implementations with classical forecasting methods and model-free reinforcement learning baselines.
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