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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Vydané v:	MethodsX Ročník 15; s. 103470
Hlavní autori:	Klanatsky, Peter, Veynandt, François, Heschl, Christian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.12.2025 Elsevier
Predmet:	Building energy management Data-driven building modelling Data-driven predictive control Energy Model predictive control Optimization algorithm Optimization problem formulation with Mixed-Integer Linear Programming for Data-driven Model Predictive Control of buildings with Thermally Activated Building Structures and shading system Smart shading control Thermally activated building structure Smart shading control Optimization problem formulation with Mixed-Integer Linear Programming for Data-driven Model Predictive Control of buildings with Thermally Activated Building Structures and shading system Model predictive control Optimization algorithm Thermally activated building structure Building energy management Data-driven predictive control Data-driven building modelling
ISSN:	2215-0161, 2215-0161
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Abstract	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. [Display omitted]
AbstractList	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. 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.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. 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. [Display omitted] 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. Image, graphical abstract
ArticleNumber	103470
Author	Veynandt, François Klanatsky, Peter Heschl, Christian
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Cites_doi	10.1016/j.enbuild.2015.06.012 10.1016/j.jprocont.2018.12.015 10.1016/j.adapen.2022.100099 10.1016/j.enbuild.2019.04.029 10.1016/j.buildenv.2016.07.007 10.1016/j.buildenv.2013.11.016 10.1016/j.jprocont.2020.02.007 10.1016/j.enbuild.2022.112709 10.1016/j.enbuild.2024.113895 10.1016/j.enbuild.2018.04.062 10.1016/j.enbuild.2014.05.053 10.1016/j.enbuild.2011.09.022 10.1016/j.arcontrol.2020.09.001 10.1061/9780784483961.047 10.1016/j.dib.2023.109891 10.1016/j.enbuild.2023.113624 10.1016/j.enbuild.2025.115590 10.1016/j.ifacol.2019.08.239 10.3390/s24134405 10.1016/j.buildenv.2021.107830 10.1016/j.rser.2016.11.167 10.1016/j.rser.2021.110969 10.1007/s00170-021-07682-3 10.3390/en11030631 10.1109/TCST.2015.2415411
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Keywords	Smart shading control Optimization problem formulation with Mixed-Integer Linear Programming for Data-driven Model Predictive Control of buildings with Thermally Activated Building Structures and shading system Model predictive control Optimization algorithm Thermally activated building structure Building energy management Data-driven predictive control Data-driven building modelling
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Snippet	Integrating renewable energy sources into buildings requires advanced control strategies to enhance demand-side flexibility. Data-driven Model Predictive...
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SubjectTerms	Building energy management Data-driven building modelling Data-driven predictive control Energy Model predictive control Optimization algorithm Optimization problem formulation with Mixed-Integer Linear Programming for Data-driven Model Predictive Control of buildings with Thermally Activated Building Structures and shading system Smart shading control Thermally activated building structure
Title	A reliable mixed-integer linear programming formulation for data-driven model predictive control in buildings
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