[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 21, Issue 3 (12-2018) ::
IJE 2018, 21(3): 87-100 Back to browse issues page
Estimating Efficiency of Monocrystalline and Polycrystalline Photovoltaic Panels Using Neural Network Models
Mostafa Zamani Mohiabadi *, Rasoul Jahromi, Majid Hasani Dastjerdi, Ehsan Mehrabi Gouhari
, m.zamani@vru.ac.ir
Abstract:   (1699 Views)
The energy production analysis of a  photovoltaic system depends on the panels tempreture and solar radiation. An endless and free source of solar energy received at the Earth's surface depends on the geographical location, different hours of day and seasons of the year.Hence, its correct evaluation is a strategic factor for the feasibility of a solar system. in this paper, a new method of energy modeling of photovoltaic systems is proposed by using the radiation and temperature data obtained from monitoring of monocrystalline and polycrystalline solar panels installed at the solar site of the Vali-e Asr university of Rafsanjan. The model is derived using data in a period of one year of the solar site by ANN models which is trained and tested by a multi - layer Perceptron neural network. The inputs of the model include the temperature of the panel and the direct solar radiation and Its output is the production power of both monocrystalline and polycrystalline solar panels of this solar site. The results showed that, it is proper to chose The activation function at the hidden layers of logsig, tansig, tansig with the number of [10 10 10] neurons.
Keywords: Modelling, Monocrystal, Polycrystal, Neural Network
Full-Text [PDF 771 kb]   (413 Downloads)    
Type of Study: Research | Subject: Renewable Energy Technologies
Received: 2015/08/14 | Accepted: 2019/05/29 | Published: 2018/12/1
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zamani Mohiabadi M, Jahromi R, Hasani Dastjerdi M, Mehrabi Gouhari E. Estimating Efficiency of Monocrystalline and Polycrystalline Photovoltaic Panels Using Neural Network Models. IJE 2018; 21 (3) :87-100
URL: http://necjournals.ir/article-1-909-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 21, Issue 3 (12-2018) Back to browse issues page
نشریه انرژی ایران Iranian Journal of Energy
Persian site map - English site map - Created in 0.07 seconds with 30 queries by YEKTAWEB 4570