Open Access
| Issue |
E3S Web Conf.
Volume 689, 2026
14th International Symposium on Heating, Ventilation, and Air Conditioning (ISHVAC 2025)
|
|
|---|---|---|
| Article Number | 08008 | |
| Number of page(s) | 7 | |
| Section | HVAC System Modelling, Simulation, and Evaluation | |
| DOI | https://doi.org/10.1051/e3sconf/202668908008 | |
| Published online | 21 January 2026 | |
- X. Wei, G. Xu, & A. Kusiak, Modeling and optimization of a chiller plant. Energy, 73, 898-907 (2014) [Google Scholar]
- J. Drgoňa, J. Arroyo, I. C. Figueroa, D. Blum, K. Arendt, D. Kim, ... & L. Helsen, All you need to know about model predictive control for buildings. Annual Reviews in Control, 50, 190-232 (2020) [CrossRef] [Google Scholar]
- G. Serale, M. Fiorentini, A. Capozzoli, D. Bernardini, & A. Bemporad. Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, 11(3), 631 (2018) [CrossRef] [Google Scholar]
- ASHRAE, ASHRAE Handbook—Fundamentals. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA, USA (2013) [Google Scholar]
- U.S. Department of Energy (DOE), EnergyPlus Engineering Reference: The Reference to EnergyPlus Calculations. U.S. Department of Energy, Washington, DC, USA (2010) [Google Scholar]
- J. H. Kim, Y. S. Kim, H. G. Jo, E. Urabe, J. Mun, Y. Shin, ... & C. S. Park, Accuracy, generalizability, and extrapolation ability of physics-based, data-driven, and hybrid models for real-life cooling towers. Building and Environment, 112756 (2025) [Google Scholar]
- A. Lazrak, F. Boudehenn, S. Bonnot, G. Fraisse, A. Leconte, P. Papillon, & B. Souyri, Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation. Renewable Energy, 86, 1009-1022 (2016) [Google Scholar]
- H. D. Vu, K. S. Chai, B. Keating, N. Tursynbek, B. Xu, K. Yang, ... & Z. Zhang, Data driven chiller plant energy optimization with domain knowledge. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 1309-1317 (2017) [Google Scholar]
- S. Park, K. U. Ahn, S. Hwang, S. Choi, &C. S. Park, Machine learning vs. hybrid machine learning model for optimal operation of a chiller. Science and Technology for the Built Environment, 25(2), 209-220 (2019) [Google Scholar]
- S. Kim, J. H. Kim, Y. S. Kim, S. Y. Heo, & C. S. Park, Hybrid AI chiller model using transfer learning: Aleatoric and epistemic uncertainty. Energy and Buildings, 115840 (2025) [Google Scholar]
- A. Kendall, & Y. Gal, What uncertainties do we need in bayesian deep learning for computer vision?. Advances in neural information processing systems, 30 (2017) [Google Scholar]
- K. Weiss, T. M. Khoshgoftaar, & D. Wang, A survey of transfer learning. Journal of Big data, 3, 1-40 (2016) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

