A reliable mixed-integer linear programming formulation for data-driven model predictive control in buildings.

Peter Klanatsky, François Veynandt, Christian Heschl

Abstract read

In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An integrated development framework for data-driven model predictive control of thermal zones in buildings.MethodsX · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Peter KlanatskyHochschule Burgenland University of Applied Sciences, Campus Pinkafeld, Steinamangerstraße 21, 7423 Pinkafeld, Austria.

François VeynandtHochschule Burgenland University of Applied Sciences, Campus Pinkafeld, Steinamangerstraße 21, 7423 Pinkafeld, Austria.

Christian HeschlHochschule Burgenland University of Applied Sciences, Campus Pinkafeld, Steinamangerstraße 21, 7423 Pinkafeld, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Building energy managementData-driven building modellingData-driven predictive controlModel predictive controlOptimization algorithmOptimization problem formulation with Mixed-Integer Linear Programming for Data-driven Model Predictive Control of buildings with Thermally Activated Building Structures and shading systemSmart shading controlThermally activated building structure

Identifiers

PMID40808755

DOI10.1016/j.mex.2025.103470

PMCPMC12343470

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.