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    Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector

    Bampoulas, Adamantios and Saffari, Mohammad and Pallonetto, Fabiano and Mangina, Eleni and Finn, Donal P. (2019) Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector. 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). pp. 815-818.

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    This paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort.

    Item Type: Article
    Keywords: energy flexibility; demand response; machine learning techniques; energy systems;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15610
    Identification Number:
    Depositing User: Fabiano Pallonetto
    Date Deposited: 01 Mar 2022 16:52
    Journal or Publication Title: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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