Stochastic forecasting is critical for efficient decision-making in uncertain systems, such as energy markets and finance, where estimating the full distribution of future scenarios is essential. We propose Diffusion Scenario Tree (DST), a general framework for constructing scenario trees using diffusion-based probabilistic forecasting models to provide a structured model of system evolution for control tasks. DST recursively samples future trajectories and organizes them into a tree via clustering, ensuring non-anticipativity (decisions depending only on observed history) at each stage, offering a superior representation of uncertainty compared to using predictive models solely for forecasting system evolution. We integrate DST into Model Predictive Control (MPC) and evaluate it on energy arbitrage in New York State's day-ahead electricity market. Experimental results show that our approach significantly outperforms the same optimization algorithms that use scenario trees generated by more conventional models. Furthermore, using DST for stochastic optimization yields more efficient decision policies by better handling uncertainty than deterministic and stochastic MPC variants using the same diffusion-based forecaster, and simple Model-Free Reinforcement Learning (RL) baselines.

翻译：随机预测对于能源市场和金融等不确定性系统中的高效决策至关重要，在这些系统中，估计未来情景的完整分布是必不可少的。我们提出了扩散场景树（DST），这是一个利用基于扩散的概率预测模型构建场景树的通用框架，旨在为控制任务提供系统演化的结构化模型。DST递归地采样未来轨迹，并通过聚类将其组织成树状结构，确保每个阶段满足非预见性（决策仅依赖于已观测历史），与仅使用预测模型进行系统演化预测相比，DST提供了更优的不确定性表征。我们将DST集成到模型预测控制（MPC）中，并在纽约州日前电力市场的能源套利问题上进行评估。实验结果表明，我们的方法显著优于使用传统模型生成场景树的相同优化算法。此外，与使用相同基于扩散的预测器的确定性及随机MPC变体以及简单的无模型强化学习（RL）基线相比，利用DST进行随机优化能够通过更好地处理不确定性，产生更高效的决策策略。