• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
2022, Climate Change 2022: Mitigation of Climate Change. Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change – Summary for PolicymakersGoogle Search
2 
E. Ela, V. Diakov, E. Ibanez, M. Heaney, 2013, Impacts of Variability and Uncertainty in Solar Photovoltaic Generation at Multiple TimescalesDOI
3 
B. Li, J. Zhang, 2020, A review on the integration of probabilistic solar forecasting in power systems, Solar Energy, Vol. 207, pp. 777-795DOI
4 
Y. Nie, Q. Paletta, A. Scott, L. M. Pomares, G. Arbod, S. Sgouridis, J. Lasenby, A. Brandt, 2024, Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey, Renewable and Sustainable Energy Reviews, Vol. 189DOI
5 
E. Chodakowska, J. Nazarko, Ł. Nazarko, H. S. Rabayah, R. M. Abendeh, R. Alawneh, 2023, ARIMA models in solar radiation forecasting in different geographic locations, Energies, Vol. 16DOI
6 
P. Bacher, H. Madsen, H. A. Nielsen, 2009, Online short-term solar power forecasting, Solar Energy, Vol. 83, No. 10, pp. 1772-1783DOI
7 
Q. Paletta, G. Terrén-Serrano, Y. Nie, B. Li, J. Bieker, W. Zhang, L. Dubus, S. Dev, C. Feng, 2023, Advances in solar forecasting: Computer vision with deep learning, Advances in Applied Energy, Vol. 11DOI
8 
W.-G. Park, J.-S. Kim, S.-M. Lim, C.-H. Kim, 2021, A Study on Photovoltaic Output Prediction Uncertainty and Intermittency Compensation Method, The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 7, pp. 961-968DOI
9 
O. Kwon, H. Hong, H. Jo, H. Cha, 2023, Prediction of a Floating Photovoltaic Generation Utilizing KMA Weather Forecast, The Transactions of the Korean Institute of Electrical Engineers, Vol. 72, No. 8, pp. 904-911DOI
10 
D. So, J. Moon, 2024, Multi-Fusion Deep Learning Based Multistep-Ahead Photovoltaic Power Forecasting Considering Multivariate Time Series Characteristics, The Transactions of the Korean Institute of Electrical Engineers, Vol. 73, No. 10, pp. 1617-1623DOI
11 
Y. Nie, X. Li, A. Scott, Y. Sun, V. Venugopal, A. Brandt, 2023, SKIPP’D: A Sky Images and Photovoltaic Power Generation Dataset for short-term solar forecasting, Solar Energy, Vol. 255, pp. 171-179DOI
12 
B. J. Martins, A. Cerentini, S. L. Mantelli, T. Z. L. Chaves, N. M. Branco, A. von Wangenheim, R. Rüther, J. M. Arrais, 2022, Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging, Solar Energy Advances, Vol. 2DOI
13 
D. Hendrycks, M. Mazeika, D. Wilson, K. Gimpel, 2018, Using trusted data to train deep networks on labels corrupted by severe noiseGoogle Search