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Development of a feasibility prediction tool for solar power plant installation analyses

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  • Kabalci, Ersan

Abstract

The solar energy becomes a challenging area among other renewable sources since the solar energy sources have the advantages of not causing pollution, having low maintenance cost, and not producing noise due to the absence of the moving parts. Although these advantages, the installation cost of a solar power plant is considerably high. However, feasibility analyses have a great role before installation in order to determine the most appropriate power plant site. Despite there are many methods used in feasibility analysis, this paper is focused on a new intelligent method based on an agglomerative hierarchical clustering approach. The solar irradiation and insolation parameters of Central Anatolian Region of Turkey are evaluated utilizing the intelligent feasibility analysis tool developed in this study. The clustering operation in the tool is performed by using the nearest neighbor algorithm. At the stage of determining the optimum hierarchical clustering results, Euclidean, Manhattan and Minkowski distance metrics are adapted to the tool. The achieved clustering results based on Minkowski distance metric provide the most feasible inferences to knowledge domain expert according to other distance metrics.

Suggested Citation

  • Kabalci, Ersan, 2011. "Development of a feasibility prediction tool for solar power plant installation analyses," Applied Energy, Elsevier, vol. 88(11), pages 4078-4086.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:11:p:4078-4086
    DOI: 10.1016/j.apenergy.2011.04.047
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    References listed on IDEAS

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    1. Chwieduk, Dorota A., 2009. "Recommendation on modelling of solar energy incident on a building envelope," Renewable Energy, Elsevier, vol. 34(3), pages 736-741.
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    Cited by:

    1. Baser, Furkan & Demirhan, Haydar, 2017. "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation," Energy, Elsevier, vol. 123(C), pages 229-240.
    2. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    3. Hong, Taehoon & Koo, Choongwan & Kwak, Taehyun, 2013. "Framework for the implementation of a new renewable energy system in an educational facility," Applied Energy, Elsevier, vol. 103(C), pages 539-551.
    4. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.

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