What price to pay? Auto-tuning a building MPC controller for optimal economic cost
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 18 Jan 2025.
Builds on
8 of 40 references.
References · 8
- 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
- 2023Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraintscited by 5
- 2020MPC Controller Tuning using Bayesian Optimization Techniquescited by 4
- 2023Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approachcited by 3
- 2023DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildingscited by 2
- 2024Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violations for Savings in Building Climate Controlcited by 2
- 2016On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approachcited by 1
- 2023Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerlandcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.