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Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review.

Authors :
Mohanty, Sthitapragyan
Patra, Prashanta Kumar
Sahoo, Sudhansu Sekhar
Source :
Renewable & Sustainable Energy Reviews. Apr2016, Vol. 56, p778-796. 19p.
Publication Year :
2016

Abstract

Solar radiation data plays a crucial role in solar energy research and application. It provides the vital information about the energy that strikes the earth and is highly useful for modeling and design of solar thermal technologies and solar photovoltaic applications. As Conventional energy sources are depleting day by day, it becomes necessary to use renewable energy sources like Solar, Wind, and Biomass etc. Amongst all forms of renewable energy sources, solar energy is widely accepted as it is quite abundant throughout the world. In many developing countries solar radiation data are not always available either due to the unavailability of measuring instruments or due to the absence of meteorological stations. It is also true that many countries fail to afford the costly measurement equipments and techniques involved for measuring solar radiation. Thus, it is quite essential to develop models to measure accurate solar radiation by using Various meteorological parameters such as(latitude, longitude and Altitude)/Climatological parameters (i.e. Sunshine duration, Humidity,clearness index, months, temperature, cloudiness, wind velocity, atmospheric pressure, diffuse radiation, beam radiation, Global radiation extra terrestrial radiation, evaporation) etc. The objective of this paper is to i) To Study and review the model and techniques used for prediction of solar radiation. ii) To identify the research gap and the best methods available in the literature review. iii) To recommend appropriate techniques for solar energy predictions so that researchers find it more convenient and do their research implementations using these techniques for various applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13640321
Volume :
56
Database :
Academic Search Index
Journal :
Renewable & Sustainable Energy Reviews
Publication Type :
Academic Journal
Accession number :
112264862
Full Text :
https://doi.org/10.1016/j.rser.2015.11.078