用自动调参让建筑能源控制器省钱，最多省20%
What price to pay? Auto-tuning a building MPC controller for optimal economic cost
- 用约束贝叶斯优化自动调参MPC控制器
- 相比人工调参降电耗17.46%，比规则控制降26.90%
- 实证显示选对需求响应方案可月省20.18%电费
需求侧管理（DSM）引入复杂电价，需先进控制以实现成本最小化。模型预测控制（MPC）提供解决方案，但其性能依赖于合适的超参数调优。本文提出使用约束贝叶斯优化（CONFIG）自动化该过程。案例研究显示，优化后的MPC相比规则控制器降低电费26.90%，相比人工调参的MPC降低17.46%。对真实合同的分析进一步表明，选择最优的DSM计划可使月度账单减少高达20.18%，展示了数据驱动实现显著用户节省的路径。
原文摘要 · Abstract (English)
Demand-side management (DSM) programs introduce complex pricing, requiring advanced control for cost minimization. Model Predictive Control (MPC) offers a solution but its performance hinges on appropriate hyperparameter tuning. We propose using Constrained Bayesian Optimization (CONFIG) to automate this process. In a case study, our optimized MPC reduced electricity costs by 26.90% compared to a rule-based controller and by 17.46% versus an manually tuned MPC. Analysis of real contracts further showed that optimal DSM program selection can lower monthly bills by up to 20.18%, demonstrating a data-driven path to significant consumer savings.
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