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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.10859 |
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| _version_ | 1866909008553574400 |
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| author | Yu, Jiarui Shi, Jicheng Xu, Wenjie Jones, Colin N. |
| author_facet | Yu, Jiarui Shi, Jicheng Xu, Wenjie Jones, Colin N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What price to pay? Auto-tuning a building MPC controller for optimal economic cost Yu, Jiarui Shi, Jicheng Xu, Wenjie Jones, Colin N. Systems and Control Machine Learning Optimization and Control 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. |
| title | What price to pay? Auto-tuning a building MPC controller for optimal economic cost |
| topic | Systems and Control Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2501.10859 |