Characterizing the Regional Photovoltaic Power Forecast Error in Japan: A Study of 5 Regions

[1]  Eric Wai Ming Lee,et al.  Short-term prediction of photovoltaic energy generation by intelligent approach , 2012 .

[2]  Yoshihisa Uchida,et al.  Statistical Analysis of the Smoothing Effect for Photovoltaic Systems in a Large Area , 2010 .

[3]  Yoshihisa Uchida,et al.  A Smoothing Effect on Forecasting Error of Regional PV System Output in Japan , 2011 .

[4]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[5]  E. Lorenz,et al.  Local and regional photovoltaic power prediction for large scale grid integration: Assessment of a new algorithm for snow detection , 2012 .

[6]  Joao Gari da Silva Fonseca,et al.  Analysis of Different Techniques to Set Support Vector Regression to Forecast Insolation in Tsukuba, Japan , 2013 .

[7]  S. Pelland,et al.  Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model , 2013 .

[8]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[9]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  T. Takashima,et al.  Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan , 2012 .

[12]  R. C. Williamson,et al.  Support vector regression with automatic accuracy control. , 1998 .