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