Mobile QR Code QR CODE : Korean Journal of Air-Conditioning and Refrigeration Engineering
Korean Journal of Air-Conditioning and Refrigeration Engineering

Korean Journal of Air-Conditioning and Refrigeration Engineering

ISO Journal TitleKorean J. Air-Cond. Refrig. Eng.
  • Open Access, Monthly
Open Access Monthly
  • ISSN : 1229-6422 (Print)
  • ISSN : 2465-7611 (Online)
Online Submission

Journal Search

  1. Home
  2. Archive
  3. 2024-11 (Vol.36 No.11)
  4. 10.6110/KJACR.2024.36.11.535
XML PDF INFO REF

References

1 
Intergovernmental Panel on Climate Change, 2021, Climate Change 2021: The Physical Science Basis. Available at https://www.ipcc.ch/report/ar6/wg1/.URL
2 
United Nations Framework Convention on Climate Change., 2015, Submission by the republic of korea – intended nationally determined contribution, Available at: https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/Republic%20of%20Korea%20First/INDC%20Submission%20by%20the%20Republic%20of%20Korea%20on%20June%2030.pdf.URL
3 
U.S. Energy Information Administration., 2022, Annual Energy Outlook 2022. Available at https://www.eia.gov/outlooks/aeo/pdf/06%20AEO2021%20Buildings.pdfURL
4 
LeCun, Y., Bengio, Y., and Hinton, G., 2015, Deep learning, Nature, Vol. 521, No. 7553, pp. 436-444.DOI
5 
Zhou, S. L., Shah, A. A., Leung, P. K., and Zhu, X., 2023, A Comprehensive Review of the Applications of Machine Learning for HVAC, DeCarbon, Vol. 2, p. 100023.DOI
6 
Long, L. D., 2023, An AI-driven Model for Predicting and Optimizing Energy-efficient Building Envelopes, Alexandria Engineering Journal, Vol. 79, No. 15, pp. 480-501.DOI
7 
Merabet, G. H., Essaaidi, M., Haddou, M. B., and Kabbaj, M. I., 2021, Intelligent Building Control Systems for Thermal Comfort and Energy-efficiency: A Systematic Review of Artificial Intelligence-assisted Techniques, Renewable and Sustainable Energy Reviews, Vol. 144, p. 110969.DOI
8 
Ngarambe, J., Yun, G. Y., and Santamouris, M., 2020, The use of Artificial Intelligence (AI) Methods in the Prediction of Thermal Comfort in Buildings: Energy Implications of AI-based Thermal Comfort Controls, Energy and Buildings, Vol. 211, No. 15, p. 109807.DOI
9 
Lee, D., Ooka, R., Matsuda, Y., Ikeda, S., and Choi, W., 2022, Experimental Analysis of Artificial Intelligence-based Model Predictive Control for Thermal Energy Storage under Different Cooling Load Conditions, Sustainable Cities and Society, Vol. 79, p. 103700.DOI
10 
U.S. Department of Energy, 2020, Energy Plus Version 9.5 Documentation: Getting Started with Energy Plus, Available at https://Energy Plus.net/documentation.URL
11 
MathWorks, 2024, MATLAB and Simulink: R2024a documentation, Available at https://www.mathworks.com/help/MATLAB.URL
12 
ANSI/ASHRAE/IES, 2016, ANSI/ASHRAE/IES 90.1-2016, Energy Standard for Buildings Except Low-rise Residential Buildings, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA.URL
13 
Jee, H. J., Yeon, S. H., and Lee, K. H., 2022, Machine Learning Based Simultaneous Control of Air Handling Unit Discharge Air and Condenser Water Temperatures Set-point for Minimized Cooling Energy in an Office Building, Energy and Buildings, Vol. 297, No. 15, p. 113471.DOI
14 
Lawrence Berkeley National Laboratory, 2016, Building Controls Virtual Test Bed (BCVTB), Available at https://simulationresearch.lbl.gov/bcvtbURL
15 
U.S. Department of Energy, 2020, Energy Plus Version 9.5 Documentation: External Interface Application Guide, Available at https://Energy Plus.net/documentationURL
16 
Aksu, G., Guzeller, C. O., and Eser, M. T., 2019, The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model, International Journal of Assessment Tools in Education, Vol. 6, No. 2, pp. 170-192.DOI
17 
Sarajlić, D., Abdel-Ilah, L., Fojnica, A., and Osmanović, A., 2018, Prediction of the Size of Nanoparticles and Microspore Surface Area Using Artificial Neural Network, Genetics & Applications, Vol. 1, No. 1, pp. 65-70.DOI
18 
Mendonça, L. F., Sousa, J. M. C., and Sá da Costa, J. M. G., 2006, Fault Isolation Using Fuzzy Model-based Observers, IFAC Proceedings, Vol. 39, No. 13, pp. 735-740.DOI
19 
ASHRAE, 2014, ASHRAE Guideline 14-2014 Measurement of Energy and Demand Savings, ASHRAE, Atlanta, GA, USA.URL