Remote detection and assessment of solar energy potential analysis based on available roof surface area: case study in Kahramanmaras, Turkey

Saban Yilmaz; Hasan Riza Ozcalik; Furkan Dincer

doi:10.1117/1.JRS.9.097698

12 June 2015 Remote detection and assessment of solar energy potential analysis based on available roof surface area: case study in Kahramanmaras, Turkey

Saban Yilmaz, Hasan Riza Ozcalik, Furkan Dincer

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 097698 (June 2015). https://doi.org/10.1117/1.JRS.9.097698

Abstract

Renewable energy has started to receive increasing attention due to various factors, such as scarcity of existing energy sources, high costs, global climate problems, etc. In this respect, solar energy has recently witnessed rapid developments due to its unlimited, carbon-free, environment-friendly, cost effective, and feasible nature. However, large pieces of land are necessary in order to install photovoltaic systems. Therefore, protection of arable lands and measurement of roof potentials are of vital importance in terms of reducing environment-friendly and renewable energy costs. Potential values of building roofs in Kahramanmaras province are calculated in detail thanks to various software and programs. Findings indicate that photovoltaic panels, which can generate a total amount of 430.348 MWp, can be installed on building roofs in Kahramanmaras province. Analysis of various parameters such as shading effect, safety factors, required usage area on the roofs, and tilt are also included in the calculations. This installed power may annually supply 693,977,463 kWh (≈694 GWh) for the region in question. In addition, CO₂ emission may be reduced by 18,541.975 tons. This study sets an example for other regions and thus can also be performed for them.

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Saban Yilmaz, Hasan Riza Ozcalik, and Furkan Dincer "Remote detection and assessment of solar energy potential analysis based on available roof surface area: case study in Kahramanmaras, Turkey," Journal of Applied Remote Sensing 9(1), 097698 (12 June 2015). https://doi.org/10.1117/1.JRS.9.097698

Published: 12 June 2015

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