Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis

Renewable Energy

Volumn 68, Issue , 2014, Pages 403-413

  Da Silva Fonseca Junior, Joao Gari ^a   Oozeki, Takashi ^a   Ohtake, Hideaki ^a   Shimose, Ken ichi ^a   Takashima, Takumi ^a   Ogimoto, Kazuhiko ^b  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^b INSTITUTE OF INDUSTRIAL SCIENCE   (Japan)

Author keywords

One day ahead regional forecasts; Photovoltaic systems; Principal component analysis; Smoothing effect; Support vector regression

Indexed keywords

MEAN SQUARE ERROR; PHOTOVOLTAIC CELLS; PRINCIPAL COMPONENT ANALYSIS; SOLAR ENERGY; WEATHER FORECASTING; FORECASTING; LEAST SQUARES APPROXIMATIONS; PHOTOVOLTAIC EFFECTS;

EFFECTIVE APPROACHES; PHOTOVOLTAIC POWER GENERATION; PHOTOVOLTAIC SYSTEMS; PRINCIPAL COMPONENTS; REGIONAL INFORMATION; ROOT MEAN SQUARE ERRORS; SMOOTHING EFFECTS; SUPPORT VECTOR REGRESSION (SVR); DAY-AHEAD;

PHOTOVOLTAIC EFFECTS; PRINCIPAL COMPONENT ANALYSIS;

PHOTOVOLTAIC SYSTEM; POWER GENERATION; PRINCIPAL COMPONENT ANALYSIS; SPATIOTEMPORAL ANALYSIS; ERROR ANALYSIS; FORECASTING METHOD; RENEWABLE RESOURCE; SMOOTHING;

JAPAN;

EID: 84896043087     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2014.02.018     Document Type: Article

References (27)

1. Ministry of Economy Trade and Industry of Japan. Number of new installations of renewable energy systems in Japan (flash report with data up to 2012/November), (in Japanese), [Online]. Available: [accessed 28.02.13]. http://www.meti.go.jp/press/2012/12/20121214002/20121214002.html.
2. Takenaka H., Nakajima T.Y., Higurashi A., Higuchi A., Takamura T., Pinker R.T., et al. Estimation of solar radiation using a neural network based on radiative transfer. JGeophys Res Atmos 2011, 116(D8).
3. Hammer A., Heinemann D., Lorenz E., Lückehe B. Short-term forecasting of solar radiation: a statistical approach using satellite data. Sol Energy Jul. 1999, 67(1-3):139-150.
4. Paulescu M., Badescu V., Brabec M. Tools for PV (photovoltaic) plant operators: nowcasting of passing clouds. Energy Jun. 2013, 54:104-112.
5. Lorenz E., Kühnert J., Heinemann D. Short term forecasting of solar irradiance by combining satellite data and numerical weather prediction. Proceeding of the 27th European photovoltaic solar energy conference 2012, 4401-4405.
6. Bacher P., Madsen H., Nielsen H.A. Online short-term solar power forecasting. Sol Energy Oct. 2009, 83(10):1772-1783.
7. Takahashi Masato, Mori Hiroyuki Ahybrid intelligent system approach to forecasting of PV generation output. Proceeding of the international conference on electrical engineering 2012 2012, 176-179.
8. Yona A., Senjyu T., Saber A.Y., Funabashi T., Sekine H., Kim C.H. Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. Intelligent systems applications to power systems conference 2007, 1-6.
9. Cao J.C., Cao S.H. Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 2006, 31(15):3435-3445.
10. Mellit A., Pavan A.M. A24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol Energy 2010, 84(5):807-821.
11. Fonseca J.G.da S., Oozeki T., Takashima T., Koshimizu G., Uchida Y., Ogimoto K. Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan. Prog Photovolt Res Appl 2012, 20(7):874-882.
12. Shi J., Lee W.-J., Liu Y., Yang Y., Wang P. Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans Ind Appl 2012, 48(3):1064-1069.
13. Lorenz E., Hurka J., Heinemann D., Beyer H.G. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Sel Top Appl Earth Obs Remote Sens 2009, 2(1):2-10.
14. Lorenz E., Scheidsteger T., Hurka J., Heinemann D., Kurz C. Regional PV power prediction for improved grid integration. Prog Photovolt Res Appl 2010, 19:757-771.
15. Lorenz E., Hur J., Karampela G., Hei D., Beyer H.G., Schneider M. Qualified forecast of ensemble power production by spatially dispersed grid-connected PV systems. Proceedings of the 23rd European photovoltaic solar energy conference and exhibition, Spain 2008, 3285-3291.
16. Pelland S., Galanis G., Kallos G. Solar and photovoltaic forecasting through post-processing of the global environmental multiscale numerical weather prediction model. Prog Photovolt Res Appl 2011, 21:284-296.
17. Yomiuri online. Total electricity market liberalization until 2020 . . . The government plan (in Japanese). Available: [accessed 03.04.13]. http://www.yomiuri.co.jp/atmoney/news/20130402-OYT1T00640.htm.
18. Saito K., Ishida J., Aranami K., Hara T., Segawa T., Narita M., et al. Nonhydrostatic atmospheric models and operational development at JMA. JMeteorol Soc Jpn Ser II 2007, 85B:271-304.
19. Iqbal M. Introduction to solar radiation 1984, Academic Press Inc.
20. Watt Engineering Ltd On the nature and distribution of solar radiation: prepared for Dept. of Energy, Assistant Secretary for Energy Technology, Division of Solar Technology, Environmental and Resource Assessments Branch 1978, 4th ed.
21. Samarasinghe S. Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition 2007, Auerbach Publications. 1st ed.
22. Haykin S. Neural networks and learning machines 2008, Pearson Education. 3rd ed.
23. Vapnik V. The nature of statistical learning theory 1999, Springer. 2nd ed.
24. Schölkopf B., Bartlett P., Smola A., Williamson R. Support vector regression with automatic accuracy control. Proceedings of ICANN'98, perspectives in neural computing 1998, 111-116.
25. Fonseca J.G.S., Oozeki T., Ohtake H., Shimose K., Takashima T., Ogimoto K. Analysis of different techniques to set support vector machines to forecast insolation in Tsukuba, Japan. Proceedings of the international conference on electrical engineering 2012, Japan 2012, 276-281.
26. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines 2001.
27. Fonseca J.G.S., Oozeki T., Takashima T., Ogimoto K. Study on the effectof the training period on the accuracy of insolation forecasts with artificial neural networks. Proceedings of the 22nd annual conference of Power & Energy Society, IEE of Japan, Fukuoka 2010, p. 10-1 10-2.