SMELs: A Data-driven Middleware for Smart Miscellaneous Electrical Load Management in Buildings

Balaji Kalluri, Sekhar Kondepudi, Tham Kwok Wai, Kua Harn Wei, Andreas Kamilaris

    Research output: Contribution to conferencePaperpeer-review

    1 Citation (Scopus)
    112 Downloads (Pure)


    Growth in Information and Communication Technology (ICT) has trigged an unprecedented proliferation of appliances a.k.a. Miscellaneous Electrical Loads (MELs) in buildings. Till now, managing MELs energy consumption in an optimum, cost-effective and intelligent manner in buildings remain an open-challenge. This article introduces a new supervised, data-driven middleware towards Smart Miscellaneous Electrical Load management in buildings (SMELs). It can perform automatic extraction, modeling and classification of the semantics of office appliances by analyzing aggregated electrical load signatures from several electrical outlets in the workplace environment. The results of analyzing more than 2,000 electrical load signatures from office workstations present classification performance ranging from 79.4% upto 95.8%. The preliminary findings from the study demonstrate the potential of SMELs as a middleware technology in Internet-of-Things (IoT) enabled smart buildings. The novelty of the proposed approach lies in combining the use of optimum sensors and existing data-driven techniques to extract detailed insights about appliances operation in real buildings.

    Original languageEnglish
    Number of pages7
    Publication statusPublished - 16 Aug 2018
    Event3rd IEEE Cyber Science and Technology Congress 2018 - Titania Hotel, Athens, Greece
    Duration: 12 Aug 201815 Aug 2018
    Conference number: 3


    Conference3rd IEEE Cyber Science and Technology Congress 2018
    Abbreviated titleCyberSciTech 2018
    Internet address


    • Building technology
    • Classification
    • Middleware
    • Miscellaneous electrical loads
    • Temporal features
    • Office buildings


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