# What price to pay? Auto-tuning a building MPC controller for optimal economic cost

arXiv: https://arxiv.org/abs/2501.10859 · HTML: https://mlsanity.com/paper/2501.10859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Constrained Efficient Global Optimization of Expensive Black-box Functions](https://mlsanity.com/paper/2211.00162/) (2022)
- [Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2301.12099/) (2023)
- [MPC Controller Tuning using Bayesian Optimization Techniques](https://mlsanity.com/paper/2009.14175/) (2020)
- [Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach](https://mlsanity.com/paper/2310.00758/) (2023)
- [Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland](https://mlsanity.com/paper/2307.08866/) (2023)
- [DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings](https://mlsanity.com/paper/2309.02908/) (2023)
- [Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violations for Savings in Building Climate Control](https://mlsanity.com/paper/2412.09238/) (2024)
- [On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach](https://mlsanity.com/paper/1609.09660/) (2016)

## Built on by (most cited first, 1 of 1)

- [Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework](https://mlsanity.com/paper/2605.09775/) (2026)

## 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.