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Optimization of significant insolation distribution parameters – A new approach towards BIPV system design

Author

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  • Paul, D.
  • Mandal, S.N.
  • Mukherjee, D.
  • Bhadra Chaudhuri, S.R.

Abstract

System efficiency and payback time are yet to attain a commercially viable level for solar photovoltaic energy projects. Despite huge development in prediction of solar radiation data, there is a gap in extraction of pertinent information from such data. Hence the available data cannot be effectively utilized for engineering application. This is acting as a barrier for the emerging technology. For making accurate engineering and financial calculations regarding any solar energy project, it is crucial to identify and optimize the most significant statistic(s) representing insolation availability by the Photovoltaic setup at the installation site. Quality Function Deployment (QFD) technique has been applied for identifying the statistic(s), which are of high significance from a project designer's point of view. A MATLAB™ program has been used to build the annual frequency distribution of hourly insolation over any module plane at a given location. Descriptive statistical analysis of such distributions is done through MINITABTM. For Building Integrated Photo Voltaic (BIPV) installation, similar statistical analysis has been carried out for the composite frequency distribution, which is formed by weighted summation of insolation distributions for different module planes used in the installation. Vital most influential statistic(s) of the composite distribution have been optimized through Artificial Neural Network computation. This approach is expected to open up a new horizon in BIPV system design.

Suggested Citation

  • Paul, D. & Mandal, S.N. & Mukherjee, D. & Bhadra Chaudhuri, S.R., 2010. "Optimization of significant insolation distribution parameters – A new approach towards BIPV system design," Renewable Energy, Elsevier, vol. 35(10), pages 2182-2191.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2182-2191
    DOI: 10.1016/j.renene.2010.02.026
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    Citations

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    Cited by:

    1. Seung-Joon Lee & Kyu-Jin Kim & Da-Sol Kim & Eui-Hwan Ryu & Jae Lee, 2020. "The Construction of a Mock-Up Test Building and a Statistical Analysis of the Data Acquired to Evaluate the Power Generation Performance of Photovoltaic Modules," Energies, MDPI, vol. 13(7), pages 1-21, March.
    2. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    3. Bayoumi, Mohannad & Fink, Dietrich, 2014. "Maximizing the performance of an energy generating façade in terms of energy saving strategies," Renewable Energy, Elsevier, vol. 64(C), pages 294-305.
    4. Martínez-Rubio, A. & Sanz-Adan, F. & Santamaría-Peña, J. & Martínez, Araceli, 2016. "Evaluating solar irradiance over facades in high building cities, based on LiDAR technology," Applied Energy, Elsevier, vol. 183(C), pages 133-147.
    5. Hassan Gholami & Harald Nils Røstvik & Koen Steemers, 2021. "The Contribution of Building-Integrated Photovoltaics (BIPV) to the Concept of Nearly Zero-Energy Cities in Europe: Potential and Challenges Ahead," Energies, MDPI, vol. 14(19), pages 1-22, September.
    6. Akbaş, Halil & Bilgen, Bilge, 2017. "An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs," Energy, Elsevier, vol. 125(C), pages 484-497.
    7. Bjørn Petter Jelle, 2015. "Building Integrated Photovoltaics: A Concise Description of the Current State of the Art and Possible Research Pathways," Energies, MDPI, vol. 9(1), pages 1-30, December.
    8. Wijeratne, W.M. Pabasara Upalakshi & Samarasinghalage, Tharushi Imalka & Yang, Rebecca Jing & Wakefield, Ron, 2022. "Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase," Applied Energy, Elsevier, vol. 309(C).

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