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

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

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Abstract

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.

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