Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers

References

1 
Anastasios Golnas, 2013, PV System Reliability: An Operator’s Perspective Anastasios Golnas SunEdison/MEMC, IEEE Journal of Photovoltaics, Vol. 3, No. 1, pp. 416-421DOI
2 
A. Sangwongwanich, Y. Yang, D. Sera, F. Blaabjerg, 2018, Lifetime Evaluation of Grid-Connected PV Inverters Consi- dering Panel Degradation Rates and Installation Sites, IEEE Transactions on Power Electronics, Vol. 33, No. 2, pp. 1125-1236DOI
3 
J. Shi, W. J. Lee, Y. Liu, Y. Yang, P. Wang, 2012, Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Tran- sactions on Industry Applications, Vol. 48, pp. no. 3. 1064-1069DOI
4 
S. V. Dhople, A. D. Dominguez-Garcia, Feb 2012, Estimation of Photovoltaic System Reliability and Performance Metrics, IEEE Trans. Power Systems, Vol. 27, pp. 554-563DOI
5 
N. Sharma, P. Sharma, D. Irwin, P. Shenoy, 2011, Predicting solar generation from weather forecasts using machine learning. In: Smart Grid Communications, IEEE Inter- national Conference on, IEEE 528-533DOI
6 
M. Ceci, R. Corizzo, F. Fumarola, D. Malerba, A. Rashkovska, 2017, Predictive modeling of pv energy production: How to set up the learning task for a better prediction?, IEEE Transactions on Industrial Informatics, Vol. 13, No. 3, pp. 956-966DOI
7 
J. J. Song, Y. S. Jeong, S. H. Lee, Mar 2014, Analysis of prediction model for solar power generation, Journal of Digital Convergence, Vol. 12, No. 3, pp. 243-248DOI
8 
D. H. Shin, J. H. Park, C. B. Kim, 2017, Photovoltaic Gener- ation Forecasting Using Weather Forecast and Predictive Sunshine and Radiation, Journal of Advanced Navigation TechnologyDOI
9 
S. M. Lee, W. J. Lee, 2016, Development of a System for Predicting Photovoltaic Power Generation and Detecting Defects Using Machine Learning, Proceedings of the Korea Information Processing Society Conference, Vol. 5, No. 10DOI
10 
H. J. Lee, 2016, Use of the Moving Average of the Current Weather Data for the Solar Power Generation Amount Prediction, Journal of Korea Multimedia Society, Vol. 19, No. 8DOI
11 
D Motta, AÁB Santos, BAS Machado, OGV Ribeiro-Filho, LOA Camargo, 2020, Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes, PLOS ONE, Vol. 15, No. 7DOI
12 
M. Ihme, A. L. Marsden, H. Pitsch, 2006, On the optimi- zation of artificial neural networks for application to the approximation of chemical systems, Center for Turbulence Research Annual Research BriefsGoogle Search