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. 2025-03 (Vol.37 No.03)
  4. 10.6110/KJACR.2025.37.3.107
XML PDF INFO REF

References

1 
Machado, D. O., Chicaiza, W. D., Escaño, J. M., Gallego, A. J., de Andrade, G. A., Normey-Rico, J. E., ... and Camacho, E. F., 2023, Digital Twin of an Absorption Chiller for Solar Cooling, Renewable Energy, Vol. 208, pp. 36-51.DOI
2 
Ho, W. T. and Yu, F. W., 2021, Improved Model and Optimization for the Energy Performance of Chiller System with Diverse Component Staging, Energy, Vol. 217, pp. 119376.DOI
3 
Liu, M., Fang, S., Dong, H., and Xu, C., 2021, Review of Digital Twin about Concepts, Technologies, and Industrial Applications, Journal of Manufacturing Systems, Vol. 58, pp. 346-361.DOI
4 
Singh, M., Fuenmayor, E., Hinchy, E. P., Qiao, Y., Murray, N., and Devine, D., 2021, Digital Twin: Origin to Future, Applied System Innovation, Vol. 4, No. 2, p. 36.DOI
5 
Chen, C. L., Chang, Y. C., and Chan, T. S., 2014, Applying Smart Models for Energy Saving in Optimal Chiller Loading, Energy and Buildings, Vol. 68, pp. 364-371.DOI
6 
Wang, L., Lee, E. W. M., Yuen, R. K., and Feng, W., 2019, Cooling Load Forecasting-based Predictive Optimisation for Chiller Plants, Energy and Buildings, Vol. 198, pp. 261-274.DOI
7 
Kim, J. H., Seong, N. C., and Choi, W., 2020, Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models, Energies, Vol. 13, No. 17, p. 4361.DOI
8 
Moghaddas-Zadeh, N., Farzaneh-Gord, M., Ebrahimi-Moghadam, A., and Bahnfleth, W. P., 2023, ANN-based Procedure to Obtain the Optimal Design and Operation of the Compression Chiller Network–Energy, Economic and Environmental Analysis, Journal of Building Engineering, Vol. 72, p. 106711.DOI
9 
Afram, A. and Janabi-Sharifi, F., 2014, Review of Modeling Methods for HVAC systems, Applied Thermal Engineering, Vol. 67, No. 1-2, pp. 507-519.DOI
10 
Katić, D., Krstić, H., Otković, I. I., and Begić, H., 2024, Comparing Multiple Linear Regression and Neural Network Models for Predicting Heating Energy Consumption in School Buildings in the Federation of Bosnia and Herzegovina. Journal of building engineering, p. 110728.DOI
11 
Bianco, V., Manca, O., and Nardini, S., 2013, Linear Regression Models to Forecast Electricity Consumption in Italy, Energy Sources, Part B: Economics, Planning, and Policy, Vol. 8, No. 1, pp. 86-93.DOI
12 
Mastrucci, A., Baume, O., Stazi, F., and Leopold, U., 2014, Estimating Energy Savings for the Residential Building Stock of An Entire City: A GIS-based Statistical Downscaling Approach Applied to Rotterdam, Energy and Buildings, Vol. 75, pp. 358-367.DOI
13 
Barrena, F., Montero, I., Miranda, M. T., Arranz, J. I., and Sepúlveda, F. J., 2024, Experimental Performance Evaluation of Self-consumption Photovoltaic System with Energy Storage using TRNSYS, Journal of Energy Storage, Vol. 92, p. 112147.DOI
14 
Afram, A., Janabi-Sharifi, F., Fung, A. S., and Raahemifar, K., 2017, Artificial Neural Network (ANN) Based Model Predictive Control (MPC) and Optimization of HVAC Systems: A state of the art Review and Case Study of a Residential HVAC System, Energy and Buildings, Vol. 141, pp. 96-113.DOI