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

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Title: Self-Learning Control Algorithms for Energy Systems Integration in the Residential Building Sector
Authors: Bampoulas, AdamantioSaffari, MohhamadPallonetto, FabianoMangina, EleniFinn, Donal
Permanent link: http://hdl.handle.net/10197/11532
Date: 18-Apr-2019
Online since: 2020-09-01T15:14:02Z
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Start page: 815
End page: 818
Copyright (published version): 2019 IEEE
Keywords: Internet of ThingsTemperature sensorsEnergy flexibilityDemand responseMachine learning techniquesEnergy systemsLoad managementEnergy consumptionElectric potential
DOI: 10.1109/WF-IoT.2019.8767220
Language: en
Status of Item: Peer reviewed
Is part of: IEEE 5th World Forum on Internet of Things: Conference Proceedings
Conference Details: The 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019
ISBN: 978-1-5386-4980-0
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Energy Institute Research Collection

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