DocumentCode
3101231
Title
Application of Recurrent Neural Network to Short-Term-Ahead Generating Power Forecasting for Photovoltaic System
Author
Yona, Atsushi ; Senjyu, Tomonobu ; Funabashi, Toshihisa
Author_Institution
Dept. of Electr. & Electron. Eng., Ryukyus Univ., Okinawa
fYear
2007
fDate
24-28 June 2007
Firstpage
1
Lastpage
6
Abstract
In recent years, there have been focus on environmental pollution issue resulting from consumption of fuel, e.g., coal and oil. Thus, 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 weather conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, a technique consider the insolation of each month, and confirm the validity of using neural network to predict insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only use weather data..
Keywords
photovoltaic power systems; power engineering computing; recurrent neural nets; alternative energy source; insolation estimation; mathematical model; photovoltaic system; recurrent neural network; short-term-ahead generating power forecasting; solar energy; Fuels; Neural networks; Oil pollution; Petroleum; Photovoltaic systems; Power generation; Recurrent neural networks; Solar energy; Solar power generation; Weather forecasting; insolation forecasting; neural network; power output for PV system; short-term-ahead forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Engineering Society General Meeting, 2007. IEEE
Conference_Location
Tampa, FL
ISSN
1932-5517
Print_ISBN
1-4244-1296-X
Electronic_ISBN
1932-5517
Type
conf
DOI
10.1109/PES.2007.386072
Filename
4275838
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