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AI Optimized Solar Tracking System for Green and Intelligent Building Development in an Urban Environment

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Handbook of Sustainability Science in the Future

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Abstract

Over the last decade, the cost of solar panels has gradually been reduced with the advancement of pertinent technologies and production in a large scale for extended applications as a viable means to drastically reduce carbon emissions from fossil fuel power generating facilities. While the concept of green buildings has been focusing on the energy savings in the past, installation of solar panels onto the rooftops of buildings presents an opportunity to generate incomes as a viable economic upside incentive to scale up the utilization of solar panels among buildings in an urban environment. Against this background, this chapter points out the latest solar tracking technologies that can be further optimized by AI machine learning for improved efficiency as well as economic returns from these capital investments into such technological infrastructure integrated with smart grid and energy storage facilities. The current limitation in the penetration of solar power among urban cities can be tackled by entrepreneurial firms to capitalize on the potentials of delivering an integrated solution by conducting both technical and economic feasibilities in a systemic manner.

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Acknowledgments

The research work in this paper has been supported by the Research Centre for Green Energy, Transport and Building (RCGETB) at the School of Professional Education and Executive Development at the Hong Kong Polytechnic University. RCGETB is a project supported by a grant from the Research Grants Council of the Hong Kong (Project No.: UGC/IDS(R)24/20).

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Authors and Affiliations

  1. Centre for Sustainable Business, International Business University, Toronto, ON, Canada

    Artie W. Ng

  2. Research Centre for Green Energy, Transport and Building, College of Professional & Continuing Education, The Hong Kong Polytechnic University, Hong Kong, China

    Andrew Wu & Edmund T. M. Wut

Authors
  1. Artie W. Ng
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  2. Andrew Wu
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  3. Edmund T. M. Wut
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Corresponding author

Correspondence to Artie W. Ng .

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Editors and Affiliations

  1. FTZ-ALS, HAW Hamburg, Hamburg, Hamburg, Germany

    Walter Leal Filho

  2. University of Coimbra, Coimbra, Portugal

    Anabela Marisa Azul

  3. Department of Business and Law, University of Milano-Bicocca, Milan, Milano, Italy

    Federica Doni

  4. University of Passo Fundo, São José, Rio Grande do Sul, Brazil

    Amanda Lange Salvia

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Ng, A.W., Wu, A., Wut, E.T.M. (2024). AI Optimized Solar Tracking System for Green and Intelligent Building Development in an Urban Environment. In: Leal Filho, W., Azul, A.M., Doni, F., Salvia, A.L. (eds) Handbook of Sustainability Science in the Future. Springer, Cham. https://doi.org/10.1007/978-3-030-68074-9_182-2

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  • DOI: https://doi.org/10.1007/978-3-030-68074-9_182-2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68074-9

  • Online ISBN: 978-3-030-68074-9

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

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Chapter history

  1. Latest

    AI Optimized Solar Tracking System for Green and Intelligent Building Development in an Urban Environment
    Published:
    01 December 2023

    DOI: https://doi.org/10.1007/978-3-030-68074-9_182-2

  2. Original

    AI Optimized Solar Tracking System for Green and Intelligent Building Development in an Urban Environment
    Published:
    12 November 2022

    DOI: https://doi.org/10.1007/978-3-030-68074-9_182-1

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