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 
Jeon B. K., Lee K. H., Kim E. J., 2019, Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control, Journal of the Korean Solar Energy Society, Vol. 35, No. 5, pp. 41-52DOI
2 
Nizami J. Ai-Garni A. Z., 1995, Forecasting Electric Energy Consumption Using Neural Network, Energy Policy, Vol. 23, No. 12, pp. 1097-1104DOI
3 
Chung M. H., 2017, Modeling of Solar Irradiance Forecasting using Local Meteorological, Korea Institute of Ecological Architecture and Environment, Vol. 17, No. 6, pp. 273-278Google Search
4 
Jeon B. K., Lee K. H., Kim E. J., 2018, Development of Weather Forecast Models for a Short-term Building Load Prediction, Journal of the Korean Solar Energy Society, Vol. 38, No. 1, pp. 1-11DOI
5 
Jeong J. H., Chae Y. T., 2018, Improvement for Forecasting of Photovoltaic Power Output Using Real Time Weather Data based on Machine Learning, Journal of The Korean Society of Living Environmental System, Vol. 25, No. 1, pp. 119-125Google Search
6 
Jeon B. K., Kim E. J., 2017, Short-term Load Prediction Using Artificial Network Models, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 29, No. 10, pp. 497-503DOI
7 
Kwak Y. H., Cheon S. H., Jang C. Y., Huh J. H., 2013, Real-time Energy Demand Prediction Method Using Weather Forecasting Data and Solar Model, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 25, No. 6, pp. 310-316DOI
8 
Seo D., 2010, Development of a Universal Model for Predicting Hourly Solar Radiation-Application : Evaluation of an Optimal Daylighting Controller, Ph.D Dissertation, University of Colorado-Boulder, USAGoogle Search
9 
, https://www.metoffice.gov.uk/, UK Official Meteorological Agency
10 
, https://www.weather.gov/, United States Official Meteorological Agency
11 
, https://weather.gc.ca/, Canadian Official Meteorological Agency
12 
, https://data.kma.go.kr/cmmn/main.do
13 
Lee Y. J., 2018, Development of Power Generation Amount Prediction Program for BIPV System Installed on the Vertical Wall of Apartment, Korea Institute of Ecological Architecture and Environment, Vol. 18, No. 1, pp. 150-111Google Search
14 
Zhang Q. Y., Huang Y. J., 2002, Development of Typical Year Weather Files for Chinese Locations, ASHRAE Transactions, Vol. 108, No. 2, pp. 1063-1075Google Search
15 
Hochreiter S. Schmidhuber J., 1997, Long Short-Term Memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780DOI
16 
Sarkar M., Bruyn A. D., 2021, LSTM Response Models for Direct Marketing Analytics : Replacing Feature Engineering with Deep Learning, Journal of Interactive Marketing, Vol. 53, pp. 80-95DOI
17 
Gu B., Zhang T., Meng H., Zhang J., 2021, Short-Term Forecasting and Uncertainty Analysis of Wind Power based on Long Short-Term Memory, Cloud Model and Non-Parametric Kernel Density Estimation, Vol. 164, pp. 687-708DOI
18 
Atef S., Eltawil A. B., 2020, Assessment of Stacked Unidirectional and Bidirectional Long Short-Term Memory Networks for Electricity Load Forecasting, Electric Power Systems Research, Vol. 187, No. 106489DOI
19 
Sagheer A., Kotb M., 2019, Time Series Forecasting of Petroleum Production Using Deep LSTM Recurrent Networks, Neurocomputing, Vol. 323, pp. 203-213DOI
20 
Ahn Y. S., Oh E. J., Lee Y. J., 2020, Verification of Accuracy of Ultra-short-term Forecast Data by the Korea Meteorological Administration for Prediction Building Performance, Korea Institute of Ecological Architecture and Environment, Vol. 20, No. 5, pp. 143-149Google Search
21 
, Korea Meteorological Administration, http://www.climate.go.kr.