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-citationAdaptive-predictive control strategy for HVAC systems in smart buildings – A review
Gholamzadehmir, M., Pero, C. D., Buffa, S., Fedrizzi, R., & Aste, N. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102480 · Cited by 236
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Physics-constrained deep learning of multi-zone building thermal dynamics
Drgoňa, J., Tuor, A. R., Chandan, V., & Vrabie, D. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110992 · Cited by 168
Self-citationData-driven methods for building control — A review and promising future directions
Maddalena, E. T., Lian, Y., & Jones, C. N. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104211 · Cited by 169
Sensor impacts on building and HVAC controls: A critical review for building energy performance
Bae, Y., Bhattacharya, S., Cui, B., Lee, S., Li, Y., Zhang, L., Im, P., Adetola, V., Vrabie, D., Leach, M., et al. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2021.100068 · Cited by 158
Zhou, Y., & Zheng, S. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114416 · Cited by 159
Balali, Y., Chong, A., Busch, A. W., & O’Keefe, S. G. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113496 · Cited by 162
Zhan, S., & Chong, A. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110835 · Cited by 154
Ten questions concerning reinforcement learning for building energy management
Nagy, Z., Henze, G. P., Dey, S., Arroyo, J., Helsen, L., Zhang, X., Chen, B., Amasyali, K., Kurte, K. R., Zamzam, A. S., et al. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110435 · Cited by 144
Self-citationNorouzi, A., Heidarifar, H., Borhan, H., Shahbakhti, M., & Koch, C. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105878 · Cited by 148
Deep Learning-Based Model Predictive Control for Resonant Power Converters
Lucia, S., Navarro, D., Karg, B., Sarnago, H., & Lucía, Ó. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2969729 · Cited by 139
Drgoňa, J., Picard, D., & Helsen, L. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.02.007 · Cited by 129
Self-citationEvaluation of advanced control strategies for building energy systems
Stoffel, P., Maier, L., Kümpel, A., Schreiber, T., & Müller, D. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112709 · Cited by 126
Bianchini, G., Casini, M., Pepe, D., Vicino, A., & Zanvettor, G. G. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.187 · Cited by 127
de Wilde, P. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113171 · Cited by 132
Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., & Srinivasan, R. S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110929 · Cited by 129
Smart building management system: Performance specifications and design requirements
Eini, R., Linkous, L., Zohrabi, N., & Abdelwahed, S. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102222 · Cited by 107
Yang, S., Wan, M. P., Chen, W., Ng, B. F., & Dubey, S. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116648 · Cited by 112
Afroz, Z., Shafiullah, G., Urmee, T., Shoeb, M. A., & Higgins, G. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108681 · Cited by 106
Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey
Zhang, H., Seal, S., Wu, D., Bouffard, F., & Boulet, B. (2022) · IEEE Access · doi:10.1109/access.2022.3156581 · Cited by 105
Zhou, Y., Zheng, S., & Zhang, G. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.11.037 · Cited by 97
Yang, S., Wan, M. P., Ng, B. F., Dubey, S., Henze, G. P., Chen, W., & Baskaran, K. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113920 · Cited by 95
Model Predictive Control of Internal Combustion Engines: A Review and Future Directions
Norouzi, A., Heidarifar, H., Shahbakhti, M., Koch, C. R., & Borhan, H. (2021) · Energies · doi:10.3390/en14196251 · Cited by 93
Wang, J., Li, S., Chen, H., Yuan, Y., & Huang, Y. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106204 · Cited by 81
Comparative study of data driven methods in building electricity use prediction
Zeng, A., Liu, S., & Yu, Y. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.04.029 · Cited by 79
Integration of flexibility potentials of district heating systems into electricity markets: A review
Golmohamadi, H., Larsen, K. G., Jensen, P. G., & Hasrat, I. R. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112200 · Cited by 78
Machine Learning for Smart and Energy-Efficient Buildings
Das, H. P., Lin, Y.-W., Agwan, U., Spangher, L., Devonport, A., Yang, Y., Drgoňa, J., Chong, A., Schiavon, S., & Spanos, C. J. (2024) · Environmental Data Science · doi:10.1017/eds.2023.43 · Cited by 78
Self-citationDrgoňa, J., Kiš, K., Tuor, A. R., Vrabie, D., & Klaučo, M. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.06.001 · Cited by 77
Self-citationCotrufo, N., Saloux, É., Hardy, J.-M., Candanedo, J. A., & Platon, R. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109563 · Cited by 76
Hu, R. L., Granderson, J., Auslander, D. M., & Agogino, A. M. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.10.107 · Cited by 75
Generative Design in the Built Environment
Chew, Z. X., Wong, J. Y., Tang, Y. H., Yip, C. C., & Maul, T. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105638 · Cited by 70
Michailidis, P., Michailidis, I., & Kosmatopoulos, E. B. (2025) · Energies · doi:10.3390/en18071724 · Cited by 72
Deep learning based model predictive control for compression ignition engines
Norouzi, A., Shahpouri, S., Gordon, D. C., Winkler, A., Nuss, E., Abel, D., Andert, J., Shahbakhti, M., & Koch, C. R. (2022) · Control Engineering Practice · doi:10.1016/j.conengprac.2022.105299 · Cited by 61
Benndorf, G. A., Wystrcil, D., & Réhault, N. (2018) · Applied Physics Reviews · doi:10.1063/1.5053110 · Cited by 64
Industrial, large-scale model predictive control with structured neural networks
Kumar, P., Rawlings, J. B., & Wright, S. J. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107291 · Cited by 60
Physics-informed machine learning modeling for predictive control using noisy data
Alhajeri, M. S., Abdullah, F., Wu, Z., & Christofides, P. (2022) · Chemical engineering research & design · doi:10.1016/j.cherd.2022.07.035 · Cited by 56
Serale, G., Fiorentini, M., Capozzoli, A., Cooper, P. W., & Perino, M. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.07.099 · Cited by 57
An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system
Ellis, M. J., & Chinde, V. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2020.06.008 · Cited by 57
Bibri, S. E., & Huang, J. (2025) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2025.100628 · Cited by 76
Tabares-Velasco, P. C., Speake, A., Harris, M., Newman, A. M., Vincent, T. L., & Lanahan, M. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.241 · Cited by 55
Giaouris, D., Παπαδόπουλος, Α. Ι., Patsios, C., Walker, S. L., Ziogou, C., Taylor, P., Voutetakis, S. S., Papadopoulou, S., & Seferlis, P. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.113 · Cited by 54
Pinto, G., Messina, R., Li, H., Hong, T., Piscitelli, M. S., & Capozzoli, A. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112530 · Cited by 54
Zhao, J. Y., Yang, Z., Shi, L., Liu, D., Li, H., Mi, Y., Wang, H., Feng, M., & Hutagaol, T. J. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122430 · Cited by 52
Hassanpour, H., Corbett, B., & Mhaskar, P. (2020) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2020.03.031 · Cited by 50
Endorsing domestic energy saving behavior using micro-moment classification
Alsalemi, A., Ramadan, M., Bensaali, F., Amira, A., Sardianos, C., Varlamis, I., & Dimitrakopoulos, G. J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.05.089 · Cited by 51
Zhao, J. Y., Li, J., & Yu, S. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110631 · Cited by 45
A model predictive controller for precision irrigation using discrete lagurre networks
Abioye, A. E., Abidin, M. S. Z., Aman, M. N., Mahmud, M. S. A., & Buyamin, S. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105953 · Cited by 45
Shin, M., Kim, Y., Kim, Y., Song, A., Kim, Y., & Kim, H. Y. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.111069 · Cited by 47
Recent advances in data mining and machine learning for enhanced building energy management
Zhou, X., Du, H., Xue, S., & Ma, Z. (2024) · Energy · doi:10.1016/j.energy.2024.132636 · Cited by 47
Machine learning-based reduced-order modeling and predictive control of nonlinear processes
Zhao, T., Zheng, Y., Gong, J., & Wu, Z. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2022.02.005 · Cited by 43
Bampoulas, A., Pallonetto, F., Mangina, E., & Finn, D. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118947 · Cited by 43
A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems
Cai, W., Sawant, S., Reinhardt, D., Rastegarpour, S., & Gros, S. (2023) · IEEE Access · doi:10.1109/access.2023.3346324 · Cited by 45
Long-term experimental evaluation and comparison of advanced controls for HVAC systems
Wang, X., & Dong, B. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123706 · Cited by 41
Joe, J., Dong, J., Munk, P. L., Kuruganti, T. P., & Cui, B. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102491 · Cited by 40
Machine learning algorithm selection for real-time energy management of hybrid energy ship
Gan, M., Hou, H., Wu, X., Liu, B., Yang, Y., & Xie, C. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.02.200 · Cited by 40
Scenario-based nonlinear model predictive control for building heating systems
Pippia, T., Lago, J., Coninck, R. D., & Schutter, B. D. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111108 · Cited by 39
Physics-constrained graph modeling for building thermal dynamics
Yang, Z., Gaidhane, A. D., Drgoňa, J., Chandan, V., Halappanavar, M. M., Liu, F., & Cao, Y. K. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100346 · Cited by 38
Self-citationZhao, J. Y., Chen, Z., Li, H., & Liu, D. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114919 · Cited by 41
Experimental data-driven model predictive control of a hospital HVAC system during regular use
Maddalena, E. T., Muller, S. A., dos Santos, R. M., Salzmann, C., & Jones, C. N. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112316 · Cited by 38
Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees
Drgoňa, J., Tuor, A. R., & Vrabie, D. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3368026 · Cited by 37
Self-citationGinzburg-Ganz, E., Segev, I., Balabanov, A., Segev, E., Naveh, S. K., Machlev, R., Belikov, J., Katzir, L., Keren, S., & Levron, Y. (2024) · Energies · doi:10.3390/en17215307 · Cited by 37
Dong, Z., Huang, X., Dong, Y., & Zhang, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114193 · Cited by 37
Energy optimization for regional buildings based on distributed reinforcement learning
Qin, Y., Ke, J., Wang, B., & Filaretov, G. F. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103625 · Cited by 36
Chakrabarty, A., Danielson, C., Bortoff, S. A., & Laughman, C. R. (2021) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2021.117335 · Cited by 35
Li, Y., & Tong, Z. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103017 · Cited by 34
Artificial Neural Network-Based Model Predictive Control Using Correlated Data
Hassanpour, H., Corbett, B., & Mhaskar, P. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c04339 · Cited by 34
Deep reinforcement learning control for non-stationary building energy management
Naug, A., Quiñones-Grueiro, M., & Biswas, G. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112584 · Cited by 36
Ławryńczuk, M., & Ocłoń, P. (2019) · Energy · doi:10.1016/j.energy.2019.06.062 · Cited by 31
Diesel engine air path control based on neural approximation of nonlinear MPC
Moriyasu, R., Nojiri, S., Matsunaga, A., Nakamura, T., & Jimbo, T. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104114 · Cited by 31
Zhu, J., Niu, J., Zhan, S., Tian, Z., Chong, A., Wang, H., & Zhou, H. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115299 · Cited by 30
Yewale, A., Methekar, R. N., & Agrawal, S. G. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106884 · Cited by 30
Zhang, Y., Vand, B., & Baldi, S. (2022) · Buildings · doi:10.3390/buildings12020238 · Cited by 33
Tardioli, G., Filho, R. S., Bernaud, P., & Ntimos, D. (2022) · Buildings · doi:10.3390/buildings12040475 · Cited by 32
Papias, I., Michalakopoulos, V., Sarmas, E., Marinakis, V., Antonesi, G., Cioara, T., & Anghel, I. M. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101783 · Cited by 29
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
Drgoňa, J., Tuor, A. R., Skomski, E., Vasisht, S., & Vrabie, D. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.518 · Cited by 29
Self-citationYaghoubi, E., Yaghoubi, E., Maghami, M. R., Rahebi, J., Jahromi, M. Z., Ghadami, R., & Yusupov, Z. (2025) · Processes · doi:10.3390/pr13072197 · Cited by 33
Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture
Ke, J., Qin, Y., Wang, B., Yang, S., Wu, H., Yang, H., & Zhao, X. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8849541 · Cited by 28
Meshed DC microgrid hierarchical control: A differential flatness approach
Zafeiratou, I., Prodan, I., Lefèvre, L., & Piétrac, L. (2019) · Electric Power Systems Research · doi:10.1016/j.epsr.2019.106133 · Cited by 27
Data-Driven Control: Theory and Applications
Soudbakhsh, D., Annaswamy, A. M., Wang, Y., Brunton, S. L., Gaudio, J. E., Hussain, H. S., Vrabie, D., Drgoňa, J., & Filev, D. (2023) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc55779.2023.10156081 · Cited by 27
Self-citationCao, S. M., Hou, S., Yu, L., & Lu, J. (2019) · Energy Science & Engineering · doi:10.1002/ese3.341 · Cited by 26
From Building Information Modelling to Mixed Reality
Brusaporci, S., Graziosi, F., Franchi, F., Maiezza, P., & Tata, A. (2020) · Springer tracts in civil engineering · doi:10.1007/978-3-030-49278-6 · Cited by 26
Vivian, J., Croci, L., & Zarrella, A. (2022) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2022.118693 · Cited by 26
Generalized reinforcement learning for building control using Behavioral Cloning
Lee, Z. E., & Zhang, K. M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117602 · Cited by 28
Nurbayeva, A., Shintemirov, A., & Rubagotti, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3217833 · Cited by 25
Machine learning meets process control: Unveiling the potential of LSTMc
Sitapure, N., & Kwon, J. S. (2024) · AIChE Journal · doi:10.1002/aic.18356 · Cited by 29
Coordinated control for Air Handling Unit and Variable Air Volume boxes in multi-zone HVAC system
Ma, S., Zou, Y., & Li, S. (2021) · Journal of Process Control · doi:10.1016/j.jprocont.2021.09.008 · Cited by 25
Transfer learning in building dynamics prediction
Chaudhary, G., Johra, H., Georges, L., & Austbø, B. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115384 · Cited by 23
Masero, E., Ruiz-Moreno, S., Frejo, J. R. D., Maestre, J. M., & Camacho, E. F. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105666 · Cited by 22
Deep learning-based model predictive control for real-time supply chain optimization
Wang, J., Swartz, C. L. E., & Huang, K. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103049 · Cited by 23
Hardware Solutions for Low-Power Smart Edge Computing
Wisniewski, L. M., Bec, J., Boguszewski, G., & Gamatié, A. (2022) · Journal of Low Power Electronics and Applications · doi:10.3390/jlpea12040061 · Cited by 26
Exploratory Analysis of Machine Learning Techniques to predict Energy Efficiency in Buildings
Goyal, M., Pandey, M., & Thakur, R. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9197976 · Cited by 22
Zhu, J., Tian, Z., Niu, J., Lu, Y., Cheng, B., & Zhou, H. (2025) · Building Simulation · doi:10.1007/s12273-025-1275-1 · Cited by 21
Machine Learning for Chemical Looping: Recent Advances and Prospects
Song, Y., Teng, S., Fang, D., Lu, Y., Chen, Z., Xiao, R., & Zeng, D. (2024) · Energy & Fuels · doi:10.1021/acs.energyfuels.4c02110 · Cited by 21
Wang, Q., Wu, W., Lin, C., Yang, Y., & Wang, B. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3329820 · Cited by 20
Online learning with stability guarantees: A memory-based warm starting for real-time MPC
Schwenkel, L., Gharbi, M., Trimpe, S., & Ebenbauer, C. (2020) · Automatica · doi:10.1016/j.automatica.2020.109247 · Cited by 20
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
