Title :
Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System
Author :
Yona, Atsushi ; Senjyu, Tomonobu ; Saber, Ahmed Yousuf ; Funabashi, Toshihisa ; Sekine, Hideomi ; Kim, Chul-Hwan
Author_Institution :
Ryukyus Univ., Okinawa
Abstract :
In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.
Keywords :
neural nets; photovoltaic power systems; power engineering computing; insolation estimation method; neural network application; one-day-ahead generating power forecasting; photovoltaic system; solar energy; Batteries; Meteorology; Neural networks; Photovoltaic systems; Power generation; Recurrent neural networks; Solar energy; Solar power generation; Weather forecasting; Wind forecasting; 24 hours ahead forecasting; insolation forecasting; neural network; power output for PV system;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on
Conference_Location :
Toki Messe, Niigata
Print_ISBN :
978-986-01-2607-5
Electronic_ISBN :
978-986-01-2607-5
DOI :
10.1109/ISAP.2007.4441657
