Browse > Article
http://dx.doi.org/10.14248/JKOSSE.2020.16.1.058

Development of Solar Power Output Prediction Method using Big Data Processing Technic  

Jung, Jae Cheon (KEPCO International Nuclear Graduate School)
Song, Chi Sung (Korea Institute of Machinery and Materials)
Publication Information
Journal of the Korean Society of Systems Engineering / v.16, no.1, 2020 , pp. 58-67 More about this Journal
Abstract
A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.
Keywords
Solar Power; Output Prediction; Big Data; Accuweather; Systems Engineering; Weather Forcast; LM (Linear Model); SVM (Support Vector Method); ANN (Artificial Neural Network); RMSEP (Root Mean Square Error Prediction); MOP (Measure of Performance); TPM (Technical Performance Measure);
Citations & Related Records
연도 인용수 순위
  • Reference
1 Mark A. Beyer, Douglas Laney, The importance of 'Big Data': A Definition, Gartner, 21 June 2012.
2 John Grantz, David Reinsel, "Extracting Value from Chaos", IDC IVIEW, 2011.
3 James Manyika & Michael Chui, " Big data: The next frontier for innovation, competition, and productivity", McKinsey Global Institute, 2011.
4 Alexander Kossiakoff, William N. Sweet, Sam Seymour, Steven M. Biemer, Systems Engineering Principles and Practice, John Wiley & Sons, 2011.
5 Jae-Gon Kim 외, Daily prediction of solar power generation based on weather forecast information in Korea, IET Renewable Power Generation 11(10), 2017.
6 SHARMA, Navin, 외 "Predicting Solar Generation from Weather Forecasts Using Machine Learning", IEEE International Conference on Smart Grid Communications, 2011.
7 Wikipedia, Solar power forecasting, https://en.wikipedia.org/wiki/Solar_power_forecasting
8 A. Saberian 외, "Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks", international Journal of Photoenergy, 2014.
9 INCOSE 시스템엔지니어링 핸드북-시스템 수명주기 프로세스 및 활동지침서 4판, INCOSE-한국시스템 엔지니어링협회.
10 https://www.accuweather.com
11 https://ko.wikipedia.org/wiki/서포트벡터머신
12 Cortes, C. and Vapnik, V. "Support-vector networks", Machine Learning, 1995.
13 Vapnik, V. (2000). "Section 5.6. Support Vector Machines-The nature of statistical learning theory", Springer-Verlag New York. ISBN 978-1-4419-3160-3.
14 Press, William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, B. P. "Section 16.5. Support Vector Machines-Numerical Recipes: The Art of Scientific Computing 3판" Cambridge University Press. ISBN 978-0-521-88068-8.
15 송치성, 4차 산업혁명과 지능화 사회에서 에너지 관리시스템 구축을 위한 정책적 제안과 플랫폼 구축, 한국산업기술진흥원, 2018.