Browse > Article
http://dx.doi.org/10.12673/jant.2018.22.5.478

Forecasting of Short Term Photovoltaic Generation by Various Input Model in Supervised Learning  

Jang, Jin-Hyuk (Department of Energy IT, Gachon University)
Shin, Dong-Ha (Department of Energy IT, Gachon University)
Kim, Chang-Bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.22, no.5, 2018 , pp. 478-484 More about this Journal
Abstract
This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.
Keywords
Support vectors machine; Deep learning; Artificial neural network; Sunshine; Solar radiation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 B. H. Lee, “A study on simplified robust optimal operation of microgrids considering the uncertainty of renewable generation and loads,” The Transactions of The Korean Institute of Electrical Engineers, Vol. 66, No. 3, pp. 513-521, May. 2017   DOI
2 S. B. Rhee, K. H. Kim, and S. G. Lee, "Optimal operation scheme of microgrid system based on renewable energy resources," The Transactions of the Korean Institute of Electrical Engineers, Vol. 60, No. 8, pp. 1467-1472, Aug. 2011   DOI
3 A. J. Conejo, M. a. Plazas, R. Espinola, S. Member, and A. B. Molina, “Day ahead electricity price forecasting using the wavelet transform and ARIMA models,” IEEE Transactions On Power Systems, Vol. 20, No. 2, pp. 1035-1042, 2005.   DOI
4 D. J. Lee, J. P. Lee, C. S. Lee, J. Y. Lim, and P. S. Ji, “Development of PV power prediction algorithm using adaptive neuro-fuzzy model,” The Transactions of the Korean Institute of Electrical Engineers, Vol. 64, No. 4, pp. 246-250, Dec. 2015.   DOI
5 W. C. Cha, J. H. Park, U. R. Cho, and J. C. Kim, “Design of generation efficiency fuzzy prediction model using solar power element data,” The transactions of The Korean Institute of Electrical Engineers, Vol. 63, No. 10, pp. 1423-1427, Oct. 2014.   DOI
6 C. S. Lee, and P. S. Ji, “Development of daily PV power forecasting models using ELM,” The Transactions of the Korean Institute of Electrical Engineers , Vol. 64P, No. 3, pp. 164-168, Sep. 2015
7 S. M. Lee, and W. J. Lee, "Development of a system for predicting photovoltaic power generation and detecting defects using machine learning," KIPS Transactions on Computer and Communication Systems, Vol. 5, No. 10, pp.353-360, Oct. 2016.   DOI
8 A. Yona, T. Senjyu, T. Funabashi, P. Mandal, and C. H. Kim, “Decision technique of solar radiation prediction applying recurrent neural network for short-term ahead power output of photovoltaic system,” Smart Grid and Renewable Energy, Vol. 4, No. 6A, pp. 32-38, Apr.2013   DOI
9 Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning., Vol. 2, Vo. 1, pp. 1-127, Jan. 2009.   DOI
10 L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and Trends in Signal Processing, Vol. 7, No. 3-4, pp. 197-387, 2014.   DOI
11 Y. LeCun, Y. Bengio, and G. Hinton. "Deep learning," Nature, Vol 521.7553, pp. 436-444, 2015   DOI
12 K. H. Lee, W. J. Kim, “Forecasting of 24_hours ahead photovoltaic power output using support vector regression,” Journal of Korean Institute of Information Technology, Vol. 14, No. 3, pp. 175-183, May 2016.