Reducing Building Heat Demand Through Intelligent Control: A Comparative Simulation Study

Curtis Meister; Philipp Schuetz; Ueli Schilt

arxiv: 2606.17913 · v1 · pith:YGPFGQC2new · submitted 2026-06-16 · 📡 eess.SY · cs.SY

Reducing Building Heat Demand Through Intelligent Control: A Comparative Simulation Study

Ueli Schilt , Curtis Meister , Philipp Schuetz This is my paper

Pith reviewed 2026-06-26 22:48 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords model predictive controlbuilding heatingenergy efficiencythermal comfortRC modelobjective functionsimulation study

0 comments

The pith

A comfort-oriented MPC for building heating achieves lower total heat consumption than one minimizing heating power.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper compares two model predictive control strategies for space heating that differ only in their optimization objectives. One minimizes quadratic heating power while the other prioritizes indoor temperature tracking to maintain thermal comfort. In six-day simulations of a virtual residential building, the comfort-oriented strategy consumes less total heat energy. The difference is attributed to how the power-minimizing objective penalizes high heating rates, resulting in distinct temperature and energy profiles. The study shows that objective function choice in MPC can reduce heating demand while still meeting comfort constraints without any changes to the building structure.

Core claim

Two MPC strategies were implemented with a simplified RC model derived from synthetic data generated by an ISO 52016-1 building model. The comfort-oriented controller, which emphasizes indoor temperature tracking, achieved lower total heat consumption than the controller minimizing quadratic heating power. Both strategies satisfied comfort and system constraints, but produced different energy use and temperature variation patterns. The results indicate that the formulation of the objective function determines heating demand outcomes.

What carries the argument

Two MPC strategies that differ solely in their optimization objective: one minimizing quadratic heating power and the other prioritizing thermal comfort through temperature tracking, both using the same simplified RC internal model.

If this is right

Both MPC strategies maintain indoor comfort and respect system constraints over the simulation period.
The choice of objective function in MPC directly affects total heating energy consumption.
High comfort levels can be maintained while achieving lower heating demand without modifying the building envelope.
Objective function design is a key factor in achieving energy reductions through intelligent heating control.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Control systems could default to comfort tracking objectives to achieve energy savings if the pattern holds across buildings.
The approach might apply to other building types if the RC model can be re-parametrized from local data.
Real-world deployment would need to test whether the simulated energy difference persists under variable weather and occupancy.
Comparing these MPC variants to conventional heating-curve controllers could quantify additional savings beyond the two strategies studied.

Load-bearing premise

The simplified resistance-capacitance model parametrized from synthetic ISO 52016-1 data accurately represents the building's thermal dynamics for use as the internal model in the MPC strategies.

What would settle it

Running both controllers on a physical building with direct measurements of heat consumption and indoor temperatures over multiple days to check if the comfort-oriented strategy still uses less total heat.

Figures

Figures reproduced from arXiv: 2606.17913 by Curtis Meister, Philipp Schuetz, Ueli Schilt.

**Figure 2.** Figure 2: 1-R-1-C model representation of the building’s thermal dynamics. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Results of RC model parametrisation using sy [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Simulation A – measured input and output. The controller is optimising for minimised heating power. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Simulation B – measured input and output. The controller is optimising for temperature tracking (i.e. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

Space heating remains the dominant energy consumer in buildings. While structural retrofitting can substantially reduce demand, it is often costly and time-intensive. As an alternative, this study investigates the potential of intelligent heating control strategies to reduce heat consumption with lower investment and faster implementation. Previous studies have shown that replacing conventional heating-curve-based controllers with model predictive controllers (MPCs) can reduce heating energy demand. Whereas most studies compare MPC to conventional control, this work evaluates two MPC strategies with different control objectives and quantifies their impact on indoor temperature tracking and heating demand. A virtual residential building model was developed in Python based on ISO 52016-1 to generate synthetic measurement data. A simplified resistance-capacitance (RC) model was parametrised using this dataset and used as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in their optimisation objective: one minimises quadratic heating power, while the other prioritises indoor temperature tracking for thermal comfort. Simulations over six days show that both strategies satisfy comfort and system constraints, but differ in energy use and temperature variation. The comfort-oriented controller achieves lower total heat consumption than the controller minimising heating power, which is attributed to the penalisation of high heating rates in the quadratic objective function. The results demonstrate the importance of objective function formulation in MPC design and show that high comfort levels can be maintained while achieving lower heating demand without structural modifications to the building envelope.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows that a comfort-prioritizing MPC uses less heat than a power-minimizing one in six-day simulation, but the closed-loop plant model is not clearly identified.

read the letter

The main point is that this simulation finds the comfort-oriented MPC consumes less total heat than the quadratic-power version, which the authors tie to penalization of high heating rates.

The work is new in its direct side-by-side of two MPC objectives that differ only in the cost function, rather than the more common MPC-versus-rule-based comparison. It sets up the RC internal model from ISO 52016-1 synthetic data, implements both controllers in MATLAB, and reports that both satisfy comfort and actuator constraints while producing different energy and temperature outcomes.

The setup is coherent for what it is. The workflow from data generation to controller comparison is documented enough to follow.

The soft spot is the plant dynamics used for the reported trajectories. The abstract states the RC model serves as the internal model for both MPCs but does not specify whether the closed-loop evaluation runs on the high-fidelity ISO model or on the RC model itself. If the plant is the RC model, the energy difference is simply the mathematical consequence of the two objectives under perfect match and does not test behavior under model mismatch. That leaves the applicability claim for real buildings resting on an unverified assumption. The scope—one virtual building, six days, no hardware validation—also limits how far the result travels.

This is for researchers working on MPC objective tuning for building heating. A reader already doing similar simulations could treat the objective comparison as one data point.

It deserves a serious referee because the simulation is internally consistent and the objective-function result is worth checking, even if the validation side needs work.

I would send it to peer review with a request to clarify the plant model and add at least one mismatch test.

Referee Report

1 major / 2 minor

Summary. The paper develops a virtual residential building model based on ISO 52016-1 to generate synthetic data for parametrizing a simplified RC model, which serves as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in objective: one minimizes quadratic heating power and the other prioritizes indoor temperature tracking. Six-day closed-loop simulations show both satisfy comfort and system constraints, but the comfort-oriented controller achieves lower total heat consumption, attributed to penalization of high heating rates in the quadratic objective.

Significance. If the simulation setup uses the RC model consistently for both prediction and plant dynamics, the work illustrates how objective-function design in MPC can yield lower heating demand while preserving comfort, without envelope modifications. The use of a standard ISO model for synthetic data generation supports reproducibility of the parametrization step.

major comments (1)

[Abstract / Methods] Abstract and simulation-setup description: the manuscript does not identify whether the reported closed-loop trajectories are generated with the high-fidelity ISO 52016-1 model or the RC approximation as the plant. This is load-bearing for the central claim, because an energy difference obtained under perfect model match (RC as both predictor and plant) is an artifact of the objective choice and does not address applicability to real buildings where mismatch occurs.

minor comments (2)

The abstract states that the RC model was 'parametrised using this dataset' but provides no numerical values for the thermal resistances and capacitances, nor the fitting procedure or validation metrics against the ISO data.
No explicit statement of the disturbance profiles, initial conditions, or exact constraint bounds used in the six-day simulations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below and will revise the paper to improve clarity on the simulation setup.

read point-by-point responses

Referee: [Abstract / Methods] Abstract and simulation-setup description: the manuscript does not identify whether the reported closed-loop trajectories are generated with the high-fidelity ISO 52016-1 model or the RC approximation as the plant. This is load-bearing for the central claim, because an energy difference obtained under perfect model match (RC as both predictor and plant) is an artifact of the objective choice and does not address applicability to real buildings where mismatch occurs.

Authors: We agree that the manuscript should explicitly state the plant model used for the closed-loop simulations. The study employs the parametrized RC model as both the internal MPC model and the plant dynamics, with the ISO 52016-1 model used only to generate the synthetic data for RC parametrization. We will revise the abstract and methods sections to make this clear. The simulation is intentionally a perfect-match case to isolate the impact of objective-function choice on energy use and comfort; this is a standard approach in comparative MPC studies to establish baseline behavior before addressing mismatch. While the referee correctly notes that real buildings introduce mismatch, the manuscript's claim concerns the importance of objective formulation within a consistent simulation framework, which remains valid and informative for MPC design. revision: yes

Circularity Check

0 steps flagged

No circularity: results from independent forward simulations of distinct MPC objectives

full rationale

The paper generates synthetic data once from the ISO 52016-1 model solely to parametrize the RC internal model, then runs closed-loop simulations of two MPCs that differ only in their objective functions. The reported lower heat consumption for the comfort-oriented controller is produced by executing those distinct optimizations over the six-day horizon; it does not reduce by construction to any fitted parameter, self-definition, or self-citation chain. The derivation chain consists of standard model-predictive control steps whose outputs are falsifiable simulation trajectories rather than tautological restatements of inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the RC model being an adequate internal representation and on the synthetic data faithfully capturing relevant dynamics; these are domain assumptions rather than derived quantities.

free parameters (1)

RC model thermal resistances and capacitances
Fitted to synthetic data generated by the ISO 52016-1 model; values not reported in abstract.

axioms (1)

domain assumption The simplified RC model sufficiently approximates the building thermal dynamics for MPC optimization purposes
Explicitly used as the internal model for both controllers in the abstract.

pith-pipeline@v0.9.1-grok · 5787 in / 1238 out tokens · 31325 ms · 2026-06-26T22:48:06.975921+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

26 extracted references · 15 canonical work pages

[1]

INTRODUCTION Buildings account for approximately 40% of global final energy consumption, with a large portion attributable to space heating and cooling [1]. Hydronic heating systems have traditionally been regulated by simple weather-compensation controllers (heating curve controllers) that adjust the heat supply based solely on the current outdoor ambien...
[2]

internal plant model

METHODOLOGY 2.1. Virtual building When applying model predictive control (MPC) to a building heating system, the controller relies on a mathematical model of the building's thermal dynamics, referred to as the “internal plant model”. This model is used to predict the building’s thermal r esponse over a future time horizon and determine the corresponding o...

2017
[3]

The results di ffer between the two simulations in terms of heating energy consumed, maximum heating power, and achieved room temperature deviations (Table 1)

RESULTS Apart from differing objectives, both simulations (A and B) are subject to th e same input parameters and constraints. The results di ffer between the two simulations in terms of heating energy consumed, maximum heating power, and achieved room temperature deviations (Table 1). Simulation A tries to minimise the heating power and results in a maxi...
[4]

It is observed that all the defined constraints (heating power li mits, temperature limits) can be met under the given ambient conditions

DISCUSSION AND CONCLUSIONS In this study, we compare two MPC controller s with different optimi sation objectives in a simulation study: one optimising fo r minimised heating power (A) a nd the other for thermal comfort (B). It is observed that all the defined constraints (heating power li mits, temperature limits) can be met under the given ambient condi...
[5]

In this context, the authors bear sole responsibility for th e conclusions and the results presented in this publication

ACKNOWLEDGEMENTS The research presented in this article was supported by InnoSuisse (grant:101.2 24 IP-EE) and by the Swiss Federal Office of Energy as part of the SW EET EDGE and PATHFNDR consortium. In this context, the authors bear sole responsibility for th e conclusions and the results presented in this publication
[6]

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2015
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Hajian, K

H. Hajian, K. Ahmed, and J. Kurnitski, ‘Dynami c heating control measured and simulated effects on power reduction, energy and indoor air temperat ure in an old apartment building with district heating’, Energy and Buildings , vol. 268, p. 112174, Aug. 2022, doi: 10.1016/j.enbuild.2022.112174

work page doi:10.1016/j.enbuild.2022.112174 2022
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Vrabie, and Lieve Helsen

J. Drgo ňa et al. , ‘All you need to know about model predictive control for buildings’, Annual Reviews in Control, vol. 50, pp. 190–232, 2020, doi: 10.1016/j.arcontrol.2020.09.001

work page doi:10.1016/j.arcontrol.2020.09.001 2020
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P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi, and T. Ibrahim, ‘A review on optimized control systems for building energy a nd comfort management of smart sustainable buildings’, Renewable and Sustainable Energy Reviews , vol. 34, pp. 409–429, June 2014, doi: 10.1016/j.rser.2014.03.027

work page doi:10.1016/j.rser.2014.03.027 2014
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Bünning et al., ‘Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC’, Applied Energy , vol

F. Bünning et al., ‘Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC’, Applied Energy , vol. 310, p. 118491, Mar. 2022, doi: 10.1016/j.apenergy.2021.118491. 9

work page doi:10.1016/j.apenergy.2021.118491 2022
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Hilliard, L

T. Hilliard, L. Swan, and Z. Qin, ‘Experimental implementation of whole building MPC with zone based thermal comfort adjustments’, Building and Environment, vol. 125, pp. 326–338, Nov. 2017, doi: 10.1016/j.buildenv.2017.09.003

work page doi:10.1016/j.buildenv.2017.09.003 2017
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Eini and S

R. Eini and S. Abdelwahed, ‘Learning-based M odel Predictive Control for Smart Building Thermal Management’, in 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) , Charlotte, NC, USA: IEEE, Oct. 2019, pp. 038–042. doi: 10.1109/HONET.2019.8908098

work page doi:10.1109/honet.2019.8908098 2019
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Bamdad, N

K. Bamdad, N. Mohammadzadeh, M. Cholette, and S. Perera, ‘Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm’, Buildings, vol. 13, no. 12, p. 3084, Dec. 2023, doi: 10.3390/buildings13123084

work page doi:10.3390/buildings13123084 2023
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D. Wang, Y. Chen, W. Wang, C. Gao, and Z. Wang, ‘Field test of Model Predictive Control in residential buildings for utility cost savings’, Energy and Buildings, vol. 288, p. 113026, June 2023, doi: 10.1016/j.enbuild.2023.113026

work page doi:10.1016/j.enbuild.2023.113026 2023
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R. Z. Freire, G. H. C. Oliveira, and N. Mendes, ‘Predictive controllers for thermal comfort optimization and energy savings’, Energy and Buildings, vol. 40, no. 7, pp. 1353–1365, Jan. 2008, doi: 10.1016/j.enbuild.2007.12.007

work page doi:10.1016/j.enbuild.2007.12.007 2008
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Cigler, J

J. Cigler, J. Siroky, M. Korda, and C. N. J ones, ‘On the Selection of the Most Appropriate MPC Problem Formulation for Buildings’, presented at the 11th REHVA world congress CLIMA, 2013

2013
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Široký, F

J. Široký, F. Oldewurtel, J. Cigler, and S. Prívara, ‘Experimental analysis of model predictive control for an energy efficient building heating system’, Applied Energy, vol. 88, no. 9, pp. 3079– 3087, Sept. 2011, doi: 10.1016/j.apenergy.2011.03.009

work page doi:10.1016/j.apenergy.2011.03.009 2011
[18]

Z. Qi, N. Zhou, X. Feng, and S. Abdolho sseinzadeh, ‘Optimizing space heating efficiency in sustainable building design a multi criteria deci sion making approach with model predictive control’, Sci Rep, vol. 15, no. 1, p. 27743, July 2025, doi: 10.1038/s41598-025-13325-4

work page doi:10.1038/s41598-025-13325-4 2025
[19]

Huang et al

S. Huang et al. , ‘Unveiling overlooked aspects of model predictive control for building air conditioning systems’, Journal of Building Performance Simulation , vol. 17, no. 4, pp. 510–525, July 2024, doi: 10.1080/19401493.2024.2346831

work page doi:10.1080/19401493.2024.2346831 2024
[20]

Ghawash, E

F. Ghawash, E. Blanco Viñuela, and B. Sc hofield, ‘Energy Consumption Optimisation by Using Advanced Control Algorithms’, p. 8 pages, 1.081 MB, Nov. 2023, doi: 10.18429/JACOW- ICALEPCS2023-MO3AO06

work page doi:10.18429/jacow- 2023
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International Organization for Standardization, ISO 52016-1:2017: Energy performance of buildings - Energy needs for heating and cooli ng, internal temperatures and sensible and latent heat loads - Part 1: Calculation procedures

2017
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ISO/TR 52016-2:2017), 2017

International Organization for Standardization, Energy performance of buildings – energy needs for heating and cooling, internal temperatures and sensible and latent heat loads – Part 2: Explanation and justification of ISO 52016-1 and ISO 52017-1 (ISO Standard No. ISO/TR 52016-2:2017), 2017

2017
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C. S. Lamprecht, ‘Meteostat Python’. Accessed: Oct. 10, 2023. [Online]. Available: https://github.com/meteostat/meteostat-python/tree/master

2023
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Madsen and J

H. Madsen and J. Holst, ‘Estimation of continuous-time models for the heat dynamics of a building’, Energy and Buildings, vol. 22, no. 1, pp. 67–79, Mar. 1995, doi: 10.1016/0378-7788(94)00904-X

work page doi:10.1016/0378-7788(94)00904-x 1995
[25]

Bacher and H

P. Bacher and H. Madsen, ‘Identifying suitable models for the heat dynamics of buildings’, Energy and Buildings, vol. 43, no. 7, pp. 1511–1522, July 2011, doi: 10.1016/j.enbuild.2011.02.005

work page doi:10.1016/j.enbuild.2011.02.005 2011
[26]

MathWorks, MATLAB - Model Predictive Control Toolbox. (2025). The MathWorks, Inc., Natick, MA, United States. [Online]. Available: https://ch.mathworks.com/help/mpc/

2025

[1] [1]

INTRODUCTION Buildings account for approximately 40% of global final energy consumption, with a large portion attributable to space heating and cooling [1]. Hydronic heating systems have traditionally been regulated by simple weather-compensation controllers (heating curve controllers) that adjust the heat supply based solely on the current outdoor ambien...

[2] [2]

internal plant model

METHODOLOGY 2.1. Virtual building When applying model predictive control (MPC) to a building heating system, the controller relies on a mathematical model of the building's thermal dynamics, referred to as the “internal plant model”. This model is used to predict the building’s thermal r esponse over a future time horizon and determine the corresponding o...

2017

[3] [3]

The results di ffer between the two simulations in terms of heating energy consumed, maximum heating power, and achieved room temperature deviations (Table 1)

RESULTS Apart from differing objectives, both simulations (A and B) are subject to th e same input parameters and constraints. The results di ffer between the two simulations in terms of heating energy consumed, maximum heating power, and achieved room temperature deviations (Table 1). Simulation A tries to minimise the heating power and results in a maxi...

[4] [4]

It is observed that all the defined constraints (heating power li mits, temperature limits) can be met under the given ambient conditions

DISCUSSION AND CONCLUSIONS In this study, we compare two MPC controller s with different optimi sation objectives in a simulation study: one optimising fo r minimised heating power (A) a nd the other for thermal comfort (B). It is observed that all the defined constraints (heating power li mits, temperature limits) can be met under the given ambient condi...

[5] [5]

In this context, the authors bear sole responsibility for th e conclusions and the results presented in this publication

ACKNOWLEDGEMENTS The research presented in this article was supported by InnoSuisse (grant:101.2 24 IP-EE) and by the Swiss Federal Office of Energy as part of the SW EET EDGE and PATHFNDR consortium. In this context, the authors bear sole responsibility for th e conclusions and the results presented in this publication

[6] [6]

International Energy Agency IEA, ‘Building Energy Performance Metrics - Supporting Energy Efficiency Progress in Major Economies’, France, 2015

2015

[7] [7]

Hajian, K

H. Hajian, K. Ahmed, and J. Kurnitski, ‘Dynami c heating control measured and simulated effects on power reduction, energy and indoor air temperat ure in an old apartment building with district heating’, Energy and Buildings , vol. 268, p. 112174, Aug. 2022, doi: 10.1016/j.enbuild.2022.112174

work page doi:10.1016/j.enbuild.2022.112174 2022

[8] [8]

Vrabie, and Lieve Helsen

J. Drgo ňa et al. , ‘All you need to know about model predictive control for buildings’, Annual Reviews in Control, vol. 50, pp. 190–232, 2020, doi: 10.1016/j.arcontrol.2020.09.001

work page doi:10.1016/j.arcontrol.2020.09.001 2020

[9] [9]

P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi, and T. Ibrahim, ‘A review on optimized control systems for building energy a nd comfort management of smart sustainable buildings’, Renewable and Sustainable Energy Reviews , vol. 34, pp. 409–429, June 2014, doi: 10.1016/j.rser.2014.03.027

work page doi:10.1016/j.rser.2014.03.027 2014

[10] [10]

Bünning et al., ‘Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC’, Applied Energy , vol

F. Bünning et al., ‘Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC’, Applied Energy , vol. 310, p. 118491, Mar. 2022, doi: 10.1016/j.apenergy.2021.118491. 9

work page doi:10.1016/j.apenergy.2021.118491 2022

[11] [11]

Hilliard, L

T. Hilliard, L. Swan, and Z. Qin, ‘Experimental implementation of whole building MPC with zone based thermal comfort adjustments’, Building and Environment, vol. 125, pp. 326–338, Nov. 2017, doi: 10.1016/j.buildenv.2017.09.003

work page doi:10.1016/j.buildenv.2017.09.003 2017

[12] [12]

Eini and S

R. Eini and S. Abdelwahed, ‘Learning-based M odel Predictive Control for Smart Building Thermal Management’, in 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) , Charlotte, NC, USA: IEEE, Oct. 2019, pp. 038–042. doi: 10.1109/HONET.2019.8908098

work page doi:10.1109/honet.2019.8908098 2019

[13] [13]

Bamdad, N

K. Bamdad, N. Mohammadzadeh, M. Cholette, and S. Perera, ‘Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm’, Buildings, vol. 13, no. 12, p. 3084, Dec. 2023, doi: 10.3390/buildings13123084

work page doi:10.3390/buildings13123084 2023

[14] [14]

D. Wang, Y. Chen, W. Wang, C. Gao, and Z. Wang, ‘Field test of Model Predictive Control in residential buildings for utility cost savings’, Energy and Buildings, vol. 288, p. 113026, June 2023, doi: 10.1016/j.enbuild.2023.113026

work page doi:10.1016/j.enbuild.2023.113026 2023

[15] [15]

R. Z. Freire, G. H. C. Oliveira, and N. Mendes, ‘Predictive controllers for thermal comfort optimization and energy savings’, Energy and Buildings, vol. 40, no. 7, pp. 1353–1365, Jan. 2008, doi: 10.1016/j.enbuild.2007.12.007

work page doi:10.1016/j.enbuild.2007.12.007 2008

[16] [16]

Cigler, J

J. Cigler, J. Siroky, M. Korda, and C. N. J ones, ‘On the Selection of the Most Appropriate MPC Problem Formulation for Buildings’, presented at the 11th REHVA world congress CLIMA, 2013

2013

[17] [17]

Široký, F

J. Široký, F. Oldewurtel, J. Cigler, and S. Prívara, ‘Experimental analysis of model predictive control for an energy efficient building heating system’, Applied Energy, vol. 88, no. 9, pp. 3079– 3087, Sept. 2011, doi: 10.1016/j.apenergy.2011.03.009

work page doi:10.1016/j.apenergy.2011.03.009 2011

[18] [18]

Z. Qi, N. Zhou, X. Feng, and S. Abdolho sseinzadeh, ‘Optimizing space heating efficiency in sustainable building design a multi criteria deci sion making approach with model predictive control’, Sci Rep, vol. 15, no. 1, p. 27743, July 2025, doi: 10.1038/s41598-025-13325-4

work page doi:10.1038/s41598-025-13325-4 2025

[19] [19]

Huang et al

S. Huang et al. , ‘Unveiling overlooked aspects of model predictive control for building air conditioning systems’, Journal of Building Performance Simulation , vol. 17, no. 4, pp. 510–525, July 2024, doi: 10.1080/19401493.2024.2346831

work page doi:10.1080/19401493.2024.2346831 2024

[20] [20]

Ghawash, E

F. Ghawash, E. Blanco Viñuela, and B. Sc hofield, ‘Energy Consumption Optimisation by Using Advanced Control Algorithms’, p. 8 pages, 1.081 MB, Nov. 2023, doi: 10.18429/JACOW- ICALEPCS2023-MO3AO06

work page doi:10.18429/jacow- 2023

[21] [21]

International Organization for Standardization, ISO 52016-1:2017: Energy performance of buildings - Energy needs for heating and cooli ng, internal temperatures and sensible and latent heat loads - Part 1: Calculation procedures

2017

[22] [22]

ISO/TR 52016-2:2017), 2017

International Organization for Standardization, Energy performance of buildings – energy needs for heating and cooling, internal temperatures and sensible and latent heat loads – Part 2: Explanation and justification of ISO 52016-1 and ISO 52017-1 (ISO Standard No. ISO/TR 52016-2:2017), 2017

2017

[23] [23]

C. S. Lamprecht, ‘Meteostat Python’. Accessed: Oct. 10, 2023. [Online]. Available: https://github.com/meteostat/meteostat-python/tree/master

2023

[24] [24]

Madsen and J

H. Madsen and J. Holst, ‘Estimation of continuous-time models for the heat dynamics of a building’, Energy and Buildings, vol. 22, no. 1, pp. 67–79, Mar. 1995, doi: 10.1016/0378-7788(94)00904-X

work page doi:10.1016/0378-7788(94)00904-x 1995

[25] [25]

Bacher and H

P. Bacher and H. Madsen, ‘Identifying suitable models for the heat dynamics of buildings’, Energy and Buildings, vol. 43, no. 7, pp. 1511–1522, July 2011, doi: 10.1016/j.enbuild.2011.02.005

work page doi:10.1016/j.enbuild.2011.02.005 2011

[26] [26]

MathWorks, MATLAB - Model Predictive Control Toolbox. (2025). The MathWorks, Inc., Natick, MA, United States. [Online]. Available: https://ch.mathworks.com/help/mpc/

2025