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. 2022-11 (Vol.34 No.11)
  4. 10.6110/KJACR.2022.34.11.533
XML PDF INFO REF

References

1 
Enríquez R., Jiménez M. J., del Rosario Heras M., 2016, Solar forecasting requirements for buildings MPC, Energy Procedia, Vol. 91, pp. 1024-1032DOI
2 
Lee H. Y., Yoon S. H., Park C. S., 2015, The Effect of Direct and Diffuse Split Models on Building Energy Simulation, Journal of the Architectural Institute of Korea, Vol. 31, No. 11, pp. 221-229DOI
3 
Seo D. H., Kim H. J., 2019, Comparative Analysis of Decomposition Models with Site-fitted Coefficients for Seoul, Journal of the Korean Solar Energy Society, Vol. 39, No. 3, pp. 91-102DOI
4 
Gueymard C. A., Ruiz-Arias J. A., 2016, Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance, Solar Energy, Vol. 128, pp. 1-30DOI
5 
Reindl D. T., Beckman W. A., Duffie J. A., 1990, Diffuse fraction correlations, Solar energy, Vol. 45, No. 1, pp. 1-7DOI
6 
Watanabe T., 1983, Procedures for separating direct and diffuse insolation on a horizontal surface and prediction of insolation on tilted surface, Transactions of the Architectural Institute of Japan, Vol. 330, pp. 96-108Google Search
7 
Batlles F. J., Rubio M. A., Tovar J., Olmo F. J., Alados-Arboledas L., 2000, Empirical modeling of hourly direct irradiance by means of hourly global irradiance, Energy, Vol. 25, No. 7, pp. 675-688DOI
8 
Korea Institute of Energy Research , 2016, New-Renewable Energy Resource Map 3.0 Standardization and Forecasting Technology Development, National Research Council of Science & Technology, NST, Vol. 12, pp. 31-37Google Search
9 
Lee H. J., Kim S. Y., Yun C. Y., 2017, Comparison of solar radiation models to estimate direct normal irradiance for Korea, Energies, Vol. 10, No. 5, pp. 594DOI
10 
Sutton R. S., Barto A. G., 2018, Reinforcement learning: An introduction, MIT pressGoogle Search
11 
Perera A. T. D., Kamalaruban P., 2021, Applications of reinforcement learning in energy systems, Renewable and Sustainable Energy Reviews, Vol. 137, pp. 110618DOI
12 
Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., Hassabis D., 2015, Human-level control through deep reinforcement learning, Nature, Vol. 518, No. 7540, pp. 529-533DOI
13 
Silver D., Huang A., Maddison C. J., Guez A., Sifre L., Van Den Driessche G., Hassabis D., 2016, Mastering the game of Go with deep neural networks and tree search, Nature, Vol. 529, No. 7587, pp. 484-489DOI
14 
Jeon B. K., Kim E. J., Shin Y., Lee K. H., 2018, Learning-based predictive building energy model using weather forecasts for optimal control of domestic energy systems, Sustainability, Vol. 11, No. 1, pp. 147DOI
15 
Jeon B. K., Kim E. J., 2020, Next-day prediction of hourly solar irradiance using local weather forecasts and LSTM trained with non-local data, Energies, Vol. 13, No. 20, pp. 5258DOI
16 
Kreider J. F., Curtiss P. S., Rabl A., 2009, Heating and cooling of buildings: Design for efficiency, CRC PressGoogle Search
17 
Duffie J. A., Beckman W. A., 2013, Solar engineering of thermal processes, John Wiley & SonsGoogle Search