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    This study introduces a cloud-based building energy management system that uses semantic middleware and advanced analytics to achieve over 25% energy savings. The system enhances building energy performance monitoring and provides optimized suggestions via a 3D web interface.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Building Science

    Background:

    • Building energy management systems (BEMS) are crucial for optimizing energy consumption and reducing operational costs.
    • Existing systems often lack seamless integration, advanced analytical capabilities, and intuitive user interfaces.
    • Semantic middleware offers a promising approach to enhance interoperability and intelligence in BEMS.

    Purpose of the Study:

    • To present a novel cloud-based BEMS.
    • To integrate an enhanced sensor network with advanced analytics.
    • To develop an intuitive Web-based user interface for building energy performance monitoring and optimization.

    Main Methods:

    • The system is structured into three layers: user interface, intelligence, and interoperability.
    • Intelligence is achieved through simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner.
    • Interoperability is enabled by a semantic knowledge base with reasoning and inference capabilities.

    Main Results:

    • The system demonstrated over 25% energy savings in a real pilot building validation.
    • Building energy performance monitoring is delivered with optimized rule suggestions.
    • A 3D Web-based interface using WebGL provides an intuitive user experience and negotiation process.

    Conclusions:

    • The proposed cloud-based BEMS, leveraging semantic middleware and advanced analytics, is effective in achieving significant energy savings.
    • The system's architecture, combining user interface, intelligence, and interoperability layers, offers a mature solution for building energy management.
    • The approach is validated and ready for further testing and deployment in diverse building environments.