A reliable mixed-integer linear programming formulation for data-driven model predictive control in buildings
Integrating renewable energy sources into buildings requires advanced control strategies to enhance demand-side flexibility. Data-driven Model Predictive Control (DMPC) has shown significant promise in this area. Buildings with Thermally Activated Building Structures (TABS) and glass façade present...
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| Vydáno v: | MethodsX Ročník 15; s. 103470 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Netherlands
Elsevier B.V
01.12.2025
Elsevier |
| Témata: | |
| ISSN: | 2215-0161, 2215-0161 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Integrating renewable energy sources into buildings requires advanced control strategies to enhance demand-side flexibility. Data-driven Model Predictive Control (DMPC) has shown significant promise in this area. Buildings with Thermally Activated Building Structures (TABS) and glass façade present flexibility potential, but have a challenging thermal balance, due to high thermal inertia and significant solar gains. In such buildings, a DMPC jointly controlling TABS and the shading system is advantageous. However, the only known implementations enabling this combination rely on a white-box model, limiting the replicability of the approach due to the required modelling effort. To facilitate the implementation of a DMPC for such a combined control task, this paper presents in details a suitable optimisation algorithm:•specifically designed for buildings with TABS and shading systems,•based on a grey-box model of thermal zones (a reduced order state-space model),•using a reliable and efficient Mixed-Integer Linear Programming (MILP) formulation,•handling thermal comfort as constraints, avoiding weighting factors in the objective function.
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2215-0161 2215-0161 |
| DOI: | 10.1016/j.mex.2025.103470 |