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AI for Sustainable University of Cebu Smart Buildings: Optimizing Energy Consumption through Machine Learning

Author

Listed:
  • Leo Bermudez

    (MSCS, College of Computer Studies, University of Cebu – Main Campus, Cebu City, Philippines)

  • Jeff Salimbangon

    (MEE-CpE, College of Computer Studies, University of Cebu – Main Campus, Cebu City, Philippines)

  • Franz Josef Caminade

    (College of Computer Studies, University of Cebu – Main Campus, Cebu City, Philippines)

Abstract

Artificial Intelligence (AI) technologies to optimize energy consumption in smart buildings. The University of Cebu, one of the leading and largest universities in Asia, is the focus of this study. This study focuses on developing an AI-based system that leverages machine learning algorithms to analyze energy usage patterns, identify inefficiencies, and recommend strategies to reduce energy consumption and promote sustainability in building operations at the University of Cebu. By collecting real-time data from sensors and integrating them with weather conditions and energy tariffs, we aim to create predictive models that optimize energy usage based on various factors. The objective of this research is to design an intelligent system that maximizes energy efficiency, reduces carbon footprint, and minimizes operating costs for the University of Cebu smart buildings. Through a combination of data analysis, algorithm development, and system implementation, we seek to provide a practical and scalable solution for sustainable energy management in a built environment. This study also addresses the ethical considerations of Artificial Intelligence adoption in smart buildings, emphasizing transparency, user Artificial Intelligence in optimizing energy consumption, and fostering environmentally friendly practices in the built environment of the University of Cebu centricity, and privacy.

Suggested Citation

  • Leo Bermudez & Jeff Salimbangon & Franz Josef Caminade, 2024. "AI for Sustainable University of Cebu Smart Buildings: Optimizing Energy Consumption through Machine Learning," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 6(4), pages 60-66, August.
  • Handle: RePEc:alk:jcsts0:v:6:y:2024:i:4:id:7893
    DOI: 10.32996/jcsts.2024.6.4.9
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