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)

References

1 
Le Quéré, C., Andrew, R. M., Friedlingstein, P., and Zhu, D., 2018, Global Carbon Budget 2017, Earth System Science Data, Vol. 10, No. 1, pp. 405-448.DOI
2 
EIA, 2021, International Energy Outlook 2021, Available: https://www.eia.gov/outlooks/ieo/.URL
3 
IEA, 2022, Buildings, Available: https://www.iea.org/reports/buildings.URL
4 
Bae, S. M., Jeong, Y. D., and Nam, Y. J., 2018, Performance Analysis of Hybrid System with PVT and GSHP for Zero Energy Building, KIEAE Journal, Vol 18, No. 5, pp. 85-92.DOI
5 
Shin, J. H. and Cho, Y. H., 2017, Predicting of the Geothermal Heat Pump System Coefficient of Performance using Artificial Neural Network, Journal of The Korean Society of Living Environmental System, Vol. 24, No. 5, pp. 562-567.DOI
6 
Lee, H. H., Choi, K. S., Kang, S. B., Oh, J. S., Sin, Y. C., and Park, K. S., 2018, Thermogravimetric Analysis Following Variety of Renewable Energy Sources, Proceedings of the KSME Conference, pp. 2967-2970.URL
7 
Jeong, D. G. and Seong, D. S., 2018, A Study on the Characteristics and the Cost of Electricity Aaccording to Energy Sources, Proceedings of KIIT Conference, pp. 379-384.URL
8 
Duffie, J. A. and Beckman, W. A., 2013, Solar Engineering of Thermal Processes. 4th ed., Wiley Publication.URL
9 
Goswami, D. Y., Kreith, F., and Kreider, J. F., 2000, Principles of Solar Engineering, Taylor and Francis.URL
10 
Kang, I. S., Moon, J. W., and Park, J. C., 2017, Recent Research Trends of Artificial Intelligent Machine Learning in Architectural Field - Review of Domestic and International Journal Papers-, Journal of the Architectural Institute of Korea Structure & Construction, Vol. 33, No. 4, pp. 63-68.DOI
11 
An, Y. J., Lee, T. K., and Kim, K. H., 2021, Prediction of Photovoltaic Power Generation Based on LSTM Considering Daylight and Solar Radiation Data, The Transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 8, pp. 1096-1101.URL
12 
Ghritlahre, H. K. and Prasad, R. K., 2018, Application of ANN Technique to Predict the Performance of Solar Collector Systems - A Review, Renewable and Sustainable Energy Reviews, Vol.84, pp.75-88.DOI
13 
Dikmen, E., Ayaz, M., Ezen, H. H., Kucuksille, Ecir, U., and Sahin, A. S., 2014, Estimation and Optimization of Thermal Performance of Evacuated Tube Solar Collector System, Heat and Mass Transfer, Vol. 50, No. 5, pp. 711-719.DOI
14 
Osorio, J. D., Wang Z., Karniadakis, G., Cai, S., Chryssostomidis, C., Panwar, M., and Hovsapian, R., 2022, Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture, Energy Conversion and Management, Vol. 252, 115063.DOI
15 
Caner, M., Gedik, E., and Kecebas, A., 2011, Investigation on Thermal Performance Calculation of Two Type Solar Air Collectors Using Artificial Neural Network. Expert Systems with Applications, Vol. 38, No. 3, pp. 1668-1674.DOI
16 
Jarimi, H., Al-Waeli, A. H. A., Razak, T. R., Bakar, M. N. A., Fazlizan, A., Ibrahim, A., and Sopian, K., 2022, Neural Network Modelling and Performance Estimation of Dual-Fluid Photovoltaic Thermal Solar Collectors in Tropical Climate Conditions. Renewable Energy, Vol. 197, pp. 1009-1019.DOI
17 
Yaïci, W. and Entchev, E., 2014, Performance Prediction of a Solar Thermal Energy System Using Artificial Neural Networks, Applied Thermal Engineering, Vol. 73, No. 1, pp. 1348-1359.DOI
18 
Xie, H., Liu, L., Ma, F., and Fan H., 2009, Performance Prediction of Solar Collectors Using Artificial Neural Networks, Conference on Artificial Intelligence and Computational Intelligence, Vol. 2, pp. 573-576.DOI
19 
Kalogirou, S. A., 2006, Prediction of Flat-Plate Collector Performance Parameters Using Artificial Neural Networks. Solar Energy, Vol. 80, No. 3, pp. 248-259.DOI
20 
Kim, D. W., Lee, D. W., Heo, J. H., and Kim, M. H., 2018, Empirical Results and Energy Self-Sufficiency Analysis of Jincheon Eco-friendly Energy Town in Cooling Season, Journal of KIAEBS, Vol. 12, No. 3, pp. 202-211.DOI
21 
Lee, D. W., Heo, J. H., and Kim, M. H., 2017, Analysis of Operating Characteristics of Large-scale Solar Thermal System Using Two Types of Collectors, Journal of the Korean Solar Energy Society, Vol. 37, No. 2, pp. 67-75.DOI
22 
Benesty J., Chen J., Huang Y., and Cohen I., 2009, Pearson Correlation Coefficient, Noise Reduction in Speech Processing, Vol. 2.DOI
23 
Python, 2022, Python Documentation, Available: https://www.python.org/.URL
24 
Anaconda, 2022, Anaconda Documentation, Available: https://www.anaconda.com/use-cases.URL
25 
Keras, 2023, Keras Documentation, Available: https://keras.io/ko/applications/.URL
26 
TensorFlow, 2023, API Documentation, https://www.tensorflow.org/api_docs.URL
27 
Lee, J. M., Hong, S. H., Seo, B. M., and Lee, K. H., 2019, Application of Artificial Neural Networks for Optimized AHU Discharge Air Temperature Set-point and Minimized Cooling Energy in VAV System, Applied Thermal Engineering, Vol. 153, pp. 726-738.DOI
28 
Park. J. W., Hong. S. H., Yeon. S. H., Seo. B. M., and Lee K. H., 2023, Predictive Model for Solar Insolation Using the Deep Learning Technique, International Journal of Energy Research, Vol. 2023, pp. 1-17.DOI
29 
ASHRAE, 2014, ASHRAE Guideline 14-2014, Measurement of Energy and Demand Savings, ASHRAE, Atlanta, GA, USA.URL
30 
Park, D. H., Kim, T. W., Byun, J. Y., and Moon, J. W., 2023, Development of Heat Storage Tank Temperature and System Energy Consumption Prediction Model for Photovoltaic Thermal and Air Source Hybrid Heat Pump, KIEAE Journal, Vol. 23, No. 1, pp. 31-36.DOI
31 
Gamarro, H., Gonzalez, J. E., and Ortiz, L. E., 2019, On the Assessment of a Numerical Weather Prediction Model for Solar Photovoltaic Power Forecasts in Cities, Journal of Energy Resources Technology, Vol. 141, No. 6, 061203.DOI
32 
Jain, A., Nandakumar, K., and Ross, A., 2005, Score Normalization in Multimodal Biometric Systems, Pattern Recognition, Vol. 38, No. 12, pp. 2270-2285.DOI
33 
Glorot, X. and Bengio, Y., 2010, Understanding the Difficulty of Training Deep Feedforward Neural Networks, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Vol. 9, pp. 249-256.URL
34 
Hastie, T., Tibshirani, R., and Friedman, J., 2009, The Elements of Statistical Learning, Springer Newyork, pp. 139-189.URL
35 
Sokolova, M. and Lapalme, G., 2009, A Systematic Analysis of Performance Measures for Classification Tasks, Information Processing & Management, Vol. 45, No. 4, pp. 427-437.DOI
36 
Kingma, D. P. and Ba, J., 2014, Adam: A Method for Stochastic Optimization, the 3rd International Conference for Learning Representations.DOI
37 
Smith, L. N., 2017, Cyclical Learning Rates for Training Neural Networks, IEEE Winter Conference on Applications of Computer Vision, pp. 464-472.DOI
38 
38. Park. J. W., Park. B. K., and Ahn. S. K., 1992, A study on the Thermal characteristics of a Thrermal Storage Tank for using Gravels, Journal of the Korean Solar Energy Society, Vol. 12, No. 1, pp. 81-87.URL