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Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes

The world is facing critical energy crisis today. As a result the conventional grid energy supplies are not enough to meet the present demand. Many advance researches are in progress to overcome this energy predicament. Power generation and management in disconnected rural villages is challengin...

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Main Authors: Onabajo, Olawale Olusegun, Tan, Chong Eng
Format: E-Article
Language:English
Published: LJS Publisher and IJCSIS Press 2013
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/16908/1/Location-based_Solar_Energy_Potential_Pr%28Abstract%29.pdf
http://ir.unimas.my/id/eprint/16908/
http://sites.google.com/site/ijcsis/
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spelling my.unimas.ir.169082017-07-19T07:24:48Z http://ir.unimas.my/id/eprint/16908/ Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes Onabajo, Olawale Olusegun Tan, Chong Eng T Technology (General) The world is facing critical energy crisis today. As a result the conventional grid energy supplies are not enough to meet the present demand. Many advance researches are in progress to overcome this energy predicament. Power generation and management in disconnected rural villages is challenging. The situation is even more challenging when landscape structure in such environment are irregular. Forces of diffusion, ground reflectance and sky view factor among others, affect the quality of final solar radiation incident on a solar panel. This paper describes the implementation of an algorithm that can be used to predict solar energy potential of irregular landscapes. Location-based Solar Energy Potential Prediction Algorithm (LOSEPPA) takes as input, the geographic latitude and longitude of the location of interest to compute the Solar Irradiance Factor (SIF). Geographic latitude plays an important role in the availability of sufficient solar radiation as well as the state of the atmosphere. Therefore, SIF value serves as a guide to the state of the atmosphere in terms of degree of cloud cover, temperature, humidity and landscape structure; which determines the feasibility of the solar energy implementation. The approach described in this paper can be used for rapidly computing the amount of solar radiation generated on a mountainous landscape surface and in the atmosphere as a function of height parameters. With SIF value known, solar panel can be mounted along specific angle of inclination to the sun. The algorithm design covers one year period and is based on the Digital Elevation Model (DEM) of the location under investigation. The proposed system was simulated using MATLAB1. Result show that the more irregular the landscape is, the lower the solar irradiance factor. SIF value of 400 and above predicts well enough sunshine for solar PV implementation in mountainous landscapes. Sample results show that solar radiation per kernel per day for a given landscape is highest between 12noon and 2.00PM local time; and the radiation per kernel per year for a given landscape have highest sunshine hours in January and December. LJS Publisher and IJCSIS Press 2013-03 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/16908/1/Location-based_Solar_Energy_Potential_Pr%28Abstract%29.pdf Onabajo, Olawale Olusegun and Tan, Chong Eng (2013) Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes. (IJCSIS) International Journal of Computer Science and Information Security, 11 (3). ISSN 1947-5500 http://sites.google.com/site/ijcsis/
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Onabajo, Olawale Olusegun
Tan, Chong Eng
Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
description The world is facing critical energy crisis today. As a result the conventional grid energy supplies are not enough to meet the present demand. Many advance researches are in progress to overcome this energy predicament. Power generation and management in disconnected rural villages is challenging. The situation is even more challenging when landscape structure in such environment are irregular. Forces of diffusion, ground reflectance and sky view factor among others, affect the quality of final solar radiation incident on a solar panel. This paper describes the implementation of an algorithm that can be used to predict solar energy potential of irregular landscapes. Location-based Solar Energy Potential Prediction Algorithm (LOSEPPA) takes as input, the geographic latitude and longitude of the location of interest to compute the Solar Irradiance Factor (SIF). Geographic latitude plays an important role in the availability of sufficient solar radiation as well as the state of the atmosphere. Therefore, SIF value serves as a guide to the state of the atmosphere in terms of degree of cloud cover, temperature, humidity and landscape structure; which determines the feasibility of the solar energy implementation. The approach described in this paper can be used for rapidly computing the amount of solar radiation generated on a mountainous landscape surface and in the atmosphere as a function of height parameters. With SIF value known, solar panel can be mounted along specific angle of inclination to the sun. The algorithm design covers one year period and is based on the Digital Elevation Model (DEM) of the location under investigation. The proposed system was simulated using MATLAB1. Result show that the more irregular the landscape is, the lower the solar irradiance factor. SIF value of 400 and above predicts well enough sunshine for solar PV implementation in mountainous landscapes. Sample results show that solar radiation per kernel per day for a given landscape is highest between 12noon and 2.00PM local time; and the radiation per kernel per year for a given landscape have highest sunshine hours in January and December.
format E-Article
author Onabajo, Olawale Olusegun
Tan, Chong Eng
author_facet Onabajo, Olawale Olusegun
Tan, Chong Eng
author_sort Onabajo, Olawale Olusegun
title Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
title_short Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
title_full Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
title_fullStr Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
title_full_unstemmed Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes
title_sort location-based solar energy potential prediction algorithm for mountainous rural landscapes
publisher LJS Publisher and IJCSIS Press
publishDate 2013
url http://ir.unimas.my/id/eprint/16908/1/Location-based_Solar_Energy_Potential_Pr%28Abstract%29.pdf
http://ir.unimas.my/id/eprint/16908/
http://sites.google.com/site/ijcsis/
_version_ 1644512484735844352
score 13.223943

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