Which price to pay? Auto-tuning building MPC controller for optimal economic cost

2025-01-18

AI Summary

Model predictive control (MPC) for building temperature regulation suffers from strong sensitivity to hyperparameters and difficulty in adapting to diverse electricity tariff structures and occupant comfort constraints. Method: This paper proposes an automated tuning framework based on constrained Bayesian optimization under the CONFIG paradigm. It is the first application of globally optimal constrained Bayesian optimization to economic MPC tuning in buildings, enabling joint optimization over black-box, user-defined comfort constraints and real-world electricity tariffs. The method integrates simplified MPC modeling, demand-side management (DSM) integration, and a black-box objective/constraint evaluation framework. Results: Experiments show that the optimized simplified MPC reduces monthly electricity costs by up to 26.90% compared to rule-based controllers. Across 12 real Belgian electricity tariffs, the framework automatically identifies optimal parameter–tariff combinations, achieving energy-cost savings of 20.18%.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to attain optimal performance under diverse contracts. However, conventional building controller design is an open-loop process without critical hyperparameter optimization, often leading to suboptimal performance due to unexpected environmental disturbances and modeling errors. Furthermore, these hyperparameters are not adapted to different pricing schemes and may lead to non-economic operations. To address these issues, we propose an efficient performance-oriented building MPC controller tuning method based on a cutting-edge efficient constrained Bayesian optimization algorithm, CONFIG, with global optimality guarantees. We demonstrate that this technique can be applied to efficiently deal with real-world DSM program selection problems under customized black-box constraints and objectives. In this study, a simple MPC controller, which offers the advantages of reduced commissioning costs, enhanced computational efficiency, was optimized to perform on a comparable level to a delicately designed and computationally expensive MPC controller. The results also indicate that with an optimized simple MPC, the monthly electricity cost of a household can be reduced by up to 26.90% compared with the cost when controlled by a basic rule-based controller under the same constraints. Then we compared 12 real electricity contracts in Belgium for a household family with customized black-box occupant comfort constraints. The results indicate a monthly electricity bill saving up to 20.18% when the most economic contract is compared with the worst one, which again illustrates the significance of choosing a proper electricity contract.

Problem

• Model Predictive Control
• Parameter Adjustment
• Cost Efficiency

Innovation

• CONFIG Algorithm
• MPC Parameter Optimization
• Cost Reduction