The DOI matches its Crossref record; 80 of the 81 references it cites were found in a registry or an index.
Approximate model predictive building control via machine learning
Open accessAbstract
Many studies have proven that the building sector can significantly benefit from replacing the current practice rule-based controllers (RBC) by more advanced control strategies like model predictive control (MPC). However, the optimization-based control algorithms, like MPC, impose increasing hardware and software requirements, together with more complicated error handling capabilities required from the commissioning staff. In recent years, several studies introduced promising remedy for these problems by using machine learning algorithms. The idea is based on devising simplified control laws learned from MPC. The main advantage of the proposed methods stems from their easy implementation even on low-level hardware. However, most of the reported studies were dealing only with problems with a limited complexity of the parametric space, and devising laws only for a single control variable, which inevitably limits their applicability to more complex building control problems. In this paper, we introduce a versatile framework for synthesis of simple, yet well-performing control strategies that mimic the behavior of optimization-based controllers, also for large scale multiple-input-multiple-output (MIMO) control problems which are common in the building sector. The approach employs multivariate regression and dimensionality reduction algorithms. Particularly, deep time delay neural networks (TDNN) and regression trees (RT) are used to derive the dependency of multiple real-valued control inputs on parameters. The complexity of the problem, as well as implementation cost, are further reduced by selecting the most significant features from the set of parameters. This reduction is based on straightforward manual selection, principal component analysis (PCA) and dynamic analysis of the building model. The approach is demonstrated on a case study employing temperature control in a six-zone building, described by a linear model with 286 states and 42 disturbances, resulting in an MPC problem with more than thousand of parameters. The results show that simplified control laws retain most of the performance of the complex MPC, while significantly decreasing the complexity and implementation cost.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To reduce GHG emissions from HVAC systems, researchers have suggested combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks [5,202] (see also 4.1).”
From Tackling climate change with machine learning (2023) · cites this paper as [202]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Goodall, C. R., & Jolliffe, I. T. (1988) · Technometrics · doi:10.2307/1270093 · Cited by 8,797
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Research works printed but not linked: 6
- Maciejowski JM. Predictive control with constraints. Prentice Hall; 2002.
- Cigler J, Gyalistras D, Široký J, Tiet V, Ferkl L. Beyond theory: the challenge of implementing model predictive control in buildings. In: Proceedings of 11th Rehva World Congress, Clima; 2013.
- Borrelli, 2003, vol. 290
- Baetens R. On externalities of heat pump-based low-energy dwellings at the low-voltage distribution grid. PhD thesis, KU Leuven, Belgium; 2015.
- Meteotest. METEONORM Version 6.1 - Edition 2009; 2009.
- Löfberg J. YALMIP: a toolbox for modeling and optimization in MATLAB. In: Proc of the CACSD conference, Taipei, Taiwan; 2004. <http://users.isy.liu.se/johanl/yalmip/>.
Cited by
Sorted by citations
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
Self-citationYao, Y., & Shekhar, D. K. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107952 · Cited by 447
Data-driven predictive control for unlocking building energy flexibility: A review
Kathirgamanathan, A., De Rosa, M., Mangina, E., & Finn, D. P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110120 · Cited by 321
Pinto, G., Wang, Z., Roy, A., Hong, T., & Capozzoli, A. (2022) · Advances in Applied Energy · doi:10.1016/j.adapen.2022.100084 · Cited by 315
Reinforced model predictive control (RL-MPC) for building energy management
Arroyo, J., Manna, C., Spiessens, F., & Helsen, L. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118346 · Cited by 259
Self-citation
Metadata
- Authors
- Drgoňa, J., Picard, D., Kvasnica, M., & Helsen, L.
- Affiliations
- KU Leuven (BE)
- Slovak University of Technology in Bratislava (SK)
- Corresponding author
- Drgoňa, Ján
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 218
- Pages
- 199-216
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2792270834
- Cited by
- 260
- References
- 81
- Citations by year
- 2026: 30 · 2025: 19 · 2024: 27 · 2023: 26 · 2022: 39 · 2021: 36 · 2020: 52 · 2019: 21 · 2018: 10
- FWCI
- 23.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Energy (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Scientific Grant Agency of the Slovak Republic (1/0403/15, 1/0112/16)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- model predictive control, building control, machine learning, multivariable control, regression trees, multivariate regression, dimensionality reduction, principal component analysis, feature selection, building temperature control, low-complexity control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
