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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Physically Consistent Neural Networks for building thermal modeling: Theory and analysis
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, et al.
Applied Energy (2022) Vol. 325, pp. 119806-119806
Open Access | Times Cited: 73

Showing 1-25 of 73 citing articles:

Interpretable machine learning for building energy management: A state-of-the-art review
Zhe Chen, Fu Xiao, Fangzhou Guo, et al.
Advances in Applied Energy (2023) Vol. 9, pp. 100123-100123
Open Access | Times Cited: 149
Physics informed neural networks for control oriented thermal modeling of buildings
Gargya Gokhale, Bert Claessens, Chris Develder
Applied Energy (2022) Vol. 314, pp. 118852-118852
Open Access | Times Cited: 83
Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization
Tianqi Xiao, Fengqi You
Applied Energy (2023) Vol. 342, pp. 121165-121165
Closed Access | Times Cited: 38
AI in HVAC fault detection and diagnosis: A systematic review
Jian Bi, Hua Wang, Enbo Yan, et al.
Energy Reviews (2024) Vol. 3, Iss. 2, pp. 100071-100071
Open Access | Times Cited: 22
A comparison between grey-box models and neural networks for indoor air temperature prediction in buildings
Jacopo Vivian, Enrico Prataviera, N. Gastaldello, et al.
Journal of Building Engineering (2024) Vol. 84, pp. 108583-108583
Closed Access | Times Cited: 16
A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: the HiLo case study
Davide Coraci, Alberto Silvestri, Giuseppe Razzano, et al.
Energy and Buildings (2025) Vol. 329, pp. 115254-115254
Open Access | Times Cited: 1
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin N. Jones, et al.
2022 American Control Conference (ACC) (2023)
Open Access | Times Cited: 21
Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control
Wei Liang, Han Li, Sicheng Zhan, et al.
Advances in Applied Energy (2024) Vol. 14, pp. 100167-100167
Open Access | Times Cited: 12
Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control
Alberto Silvestri, Davide Coraci, Silvio Brandi, et al.
Applied Energy (2024) Vol. 368, pp. 123447-123447
Open Access | Times Cited: 11
Parametric investigation and optimization of phase change material-based thermal energy storage integrated desiccant coated energy exchanger through physics informed neural networks oriented deep learning approach
Gaurav Priyadarshi, C. Murali, Sumit Agarwal, et al.
Journal of Energy Storage (2024) Vol. 80, pp. 110231-110231
Closed Access | Times Cited: 8
Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach
Wenjie Xu, Bratislav Svetozarevic, Loris Di Natale, et al.
Applied Energy (2024) Vol. 358, pp. 122493-122493
Open Access | Times Cited: 7
Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics
Giuseppe Pinto, Riccardo Messina, Han Li, et al.
Energy and Buildings (2022) Vol. 276, pp. 112530-112530
Open Access | Times Cited: 28
Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, et al.
Applied Energy (2023) Vol. 340, pp. 121071-121071
Open Access | Times Cited: 18
Physics-informed hierarchical data-driven predictive control for building HVAC systems to achieve energy and health nexus
Xuezheng Wang, Bing Dong
Energy and Buildings (2023) Vol. 291, pp. 113088-113088
Closed Access | Times Cited: 18
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks
Fabio Pavirani, Gargya Gokhale, Bert Claessens, et al.
Energy and Buildings (2024) Vol. 311, pp. 114161-114161
Open Access | Times Cited: 6
Physics-constrained graph modeling for building thermal dynamics
Ziyao Yang, Amol D. Gaidhane, Ján Drgoňa, et al.
Energy and AI (2024) Vol. 16, pp. 100346-100346
Open Access | Times Cited: 5
A Future Direction of Machine Learning for Building Energy Management: Interpretable Models
Luca Gugliermetti, Fabrizio Cumo, Sofia Agostinelli
Energies (2024) Vol. 17, Iss. 3, pp. 700-700
Open Access | Times Cited: 5
Enabling scalable Model Predictive Control design for building HVAC systems using semantic data modelling
Lu Wan, Ferdinand Rossa, Torsten Welfonder, et al.
Automation in Construction (2025) Vol. 170, pp. 105929-105929
Closed Access
Toward scalable prediction of indoor thermal dynamics: Neural-network-implanted state-space (NNiSS) model
Jeeye Mun, Hyeong-Gon Jo, Cheol Soo Park
Energy and Buildings (2025), pp. 115359-115359
Closed Access
Sensitivity Analysis of Physical Regularization in Physics-informed Neural Networks (PINNs) of Building Thermal Modeling
Yongbao Chen, Huilong Wang, Zhe Chen
Building and Environment (2025), pp. 112693-112693
Closed Access
Accuracy, generalizability, and extrapolation ability of physics-based, data-driven, and hybrid models for real-life cooling towers
Jin‐Hong Kim, Young Sub Kim, Hyunji Jo, et al.
Building and Environment (2025), pp. 112756-112756
Closed Access
Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
Jicheng Shi, Yingzhao Lian, Christophe Salzmann, et al.
Energy and Buildings (2025), pp. 115498-115498
Open Access
Physics-informed digital twin design for supporting the selection of process settings in continuous manufacturing, with a focus in fiberboard production
F. Garcia, Hendrik Devriendt, Hüseyin Metin, et al.
Computers in Industry (2025) Vol. 168, pp. 104267-104267
Closed Access
Experimental data-driven model predictive control of a hospital HVAC system during regular use
Emilio T. Maddalena, Silvio A. Müller, Rafael M. dos Santos, et al.
Energy and Buildings (2022) Vol. 271, pp. 112316-112316
Open Access | Times Cited: 23
Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network
Fangzhou Guo, Ao Li, Yue Bao, et al.
Applied Energy (2023) Vol. 354, pp. 122190-122190
Closed Access | Times Cited: 12
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