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Solar Radiation Prediction Based on the Artificial Neutral Networks Using the BP Algorithm

论文作者:留学生论文论文属性:职称论文 Scholarship Papers登出时间:2010-10-01编辑:steelbeezxp点击率:7238

论文字数:论文编号:org201010010805588562语种:英语论文 English地区:中国价格:免费论文

附件:Solar Radiation Prediction Based on the Artificial Neutral Networks Using the BP Algorithm .pdf

关键词:Solar Radiation PredictionArtificial Neutral Networks

Solar Radiation Prediction Based on the Artificial Neutral Networks Using the BP Algorithm

Abstract—Solar radiation is one of the most vital
meteorological factors in the design and analysis of photovoltaic
systems. In this paper, the Artificial Neutral Network (ANN)
was applied for the prediction of yearly total solar radiations, by
using the Back Propagation Algorithm (BP Algorithm). The
solar radiation data from 1961 to 2000 of Guangzhou were used
to train the neutral networks and the data from 2001 to 2003
were used to test the predicted values. The results proved that
the predicted values of the tested networks are in agree with the
actual values and the solar radiation data of the following years
are properly predicted by using the ANN model.
I. INTRODUCTION
radiation is a very important factor of the design and
analysis of photovoltaic systems since the solar radiation
of one district is closely relevant to the energy that the
photovoltaic system can generate. In other areas, solar
radiation also plays an important role, for example, the
determination of air-conditioning load [1, 2]. Before
designing the photovoltaic system, the data of solar radiation
of the district should be collected to determine the power of
the system and the optimum tilt angle of PV arrays since the
solar radiation has a great influence on the performance of the
photovoltaic system. In recent years, some scholars proposed
and set up many models, to calculate the total solar radiation,
the direct solar radiation and the diffuse solar radiation, such
as the lares-Pereirs & Rabl model, ASGRAE model and the
ARIMA model [3, 4, 5, 6 and 7]. However, since there are
various kinds of factors that can influence the solar radiation
and the effects of the proposed models are also affected by
other factors, such as the location and the time, thus it is rather
difficult to descript the regularity of solar radiation by using a
definitive model. That is also the reason why some times
there are significant errors when using the above models to
calculate and predict the solar radiation.
Artificial Neutral Networks show its superiority in
processing the non-linear data comparing with only using
definite models. It is a supervised learning method
extensively applied in the training of multilayered feed
forward neutral networks. It is also the most widely used
learning algorithm because of its simplicity and low
computational complexity. This paper used the BP Algorithm
Manuscript received June 9, 2008.
Gang Yang is with the Ecole Superieur d’Electricité (SUPELEC), Plateau
de Moulon, 3 rue Joliot Curie, GIF SUR YVETTE 91192, France. He was
once with the Institute for Solar Energy System, Sun Yat-sen University
(SYSU), Guangzhou, 51006, People’s Republic of China. (e-mail:
gang.yang@supelec.fr).
Yongxian Du is with the Institute for Solar Energy System, Sun Yat-sen
University (SYSU), Guangzhou, 510006, People’s Republic of China
(e-mail: st04dyx@mail2.sysu.edu.cn)..
and the collected data to investigate the prediction of solar
radiation of Guangzhou city.
II. BP ALGORITHM
Figure 1. Structure of a feed forward network with a hidden layer
Figure 1 shows the basic structure of a feed forward
network with a single hidden layer. The network consists of N
input nodes, K hidden nodes, and M output nodes. S论文英语论文网提供整理,提供论文代写英语论文代写代写论文代写英语论文代写留学生论文代写英文论文留学生论文代写相关核心关键词搜索。

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