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has been cited by the following article:

• TITLE: Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

  AUTHORS: Atsushi Yona, Tomonobu Senjyu, Toshihisa Funabashi, Paras Mandal, Chul-Hwan Kim

  KEYWORDS: Neural Network; Short-Term-Ahead Forecasting; Power Output for PV System; Solar Radiation Forecasting

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.4 No.6A, September 3, 2013

  ABSTRACT: In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other method