Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad

Mehdi Shaddel, Dawood Seyed Javan and Parisa Baghernia

Renewable and Sustainable Energy Reviews, 2016, vol. 53, issue C, 59-67

Abstract: Today, for providing clean energy, solar capturing facilities such as photovoltaic panels (PV) or solar thermal collectors (SCTs) have been increasingly installed worldwide. On the other side, lack of solar radiation data is one of the barriers for developing these technologies locally. Short-time step calculation of solar global irradiation (SGI) on inclined planes is required regarding to predict precise performance of solar systems, leading to enhance security operation's conditions and economic cost saving. Moreover, SGI values on tilted absorbers have a nonlinear relationship with several variables such as Horizontal Solar Global Irradiation, Extraterrestrial Horizontal Global Irradiation, and number of days, collector angle, solar altitude angle and the latitude of the location. Thus computation of SGI is neither readily to obtain nor easy to forecast. This paper is proposed on estimating accurate values of SGI on tilted planes via Artificial Neural Network (ANN). Indeed, ANNs are effective tools to model nonlinear systems and are widely used simulation software incorporated in MATLAB. Mashhad the second megacity of Iran is taken into account for the case study. The ANN is developed and optimized using every 30min of SGI data (6.00AM until 5.00PM) in 2013 on zero, 45° and 60° inclined planes respectively. These data have been gauged by pyranometers which are installed in Air & Solar Institute of Ferdowsi University of Mashhad. Meanwhile, the accuracies including R2 (Correlation Coefficient), MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are obtained 0.9242, 0.0284, 0.055 and 0.9302, 0.0269, 0.0549 for 60 and 45 tilted collectors respectively. Eventually it is concluded that ANN can be a reliable network and well capable for forecasting solar energy on slope solar absorbers in Mashhad.

Keywords: Prediction of global solar irradiation; Artificial neural network; Tilted solar absorber (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032115008692
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:53:y:2016:i:c:p:59-67

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2015.08.023

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().