Collaborative learning for classification and prediction of building energy flexibility

Anil Kumar, Elena Mocanu, Muhammad Babar, Phuong H. Nguyen

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    Abstract

    In this paper we propose an simple digital learning platform for flexible energy detection using data with fine granularity. The platform is empowered with artificially intelligent methods aiming to quantify the uncertainty of building energy consumption at building level, as well as at the aggregated level. Two major learning tasks are perform in this context: prediction and classification. Firstly, the building energy prediction with various time steps resolution are perform using methods such as Fully Connected Neural Networks (FCNN), Long short-term memory (LSTM), and Decision Trees (DT). Secondly, a Support Vector Machine (SVM) method is used to unlock the building energy flexibility by performing classification assuming three different levels of flexibility. Further on, a collaborative task is integrate within the platform to improve the multi-class classification accuracy. Through the end, we argue that this approach can be considered a solid integrated and automated basic block able to incorporate future AI models in (near) real-time to explore the benefits at the synergy between built environment and emerging smart grid technologies and applications.
    Original languageEnglish
    Title of host publication9th IEEE PES Innovative Smart Grid Technology Conference Europe
    PublisherIEEE
    Number of pages5
    Publication statusAccepted/In press - 2019
    Event2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe - University POLITEHNICA, Bucharest, Romania
    Duration: 29 Sep 20192 Oct 2019
    http://sites.ieee.org/isgt-europe-2019/

    Conference

    Conference2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe
    Abbreviated titleISGT 2019
    CountryRomania
    CityBucharest
    Period29/09/192/10/19
    Internet address

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  • Cite this

    Kumar, A., Mocanu, E., Babar, M., & Nguyen, P. H. (Accepted/In press). Collaborative learning for classification and prediction of building energy flexibility. In 9th IEEE PES Innovative Smart Grid Technology Conference Europe IEEE.