Pallonetto, Fabiano and De Rosa, Matteo and Milano, Federico and Finn, Donal P. (2019) Demand response algorithms for smart-grid ready residential buildings using machine learning models. Applied Energy, 239. pp. 1265-1282. ISSN 0306-2619

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Abstract

This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm).

Item Type:	Article
Additional Information:	This is the preprint version of the published article, which is available at Fabiano Pallonetto, Mattia De Rosa, Federico Milano, Donal P. Finn, Demand response algorithms for smart-grid ready residential buildings using machine learning models, Applied Energy, Volume 239, 2019, Pages 1265-1282, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2019.02.020. (https://www.sciencedirect.com/science/article/pii/S0306261919303101)
Keywords:	Building demand response; Optimisation; Machine learning; Control algorithms; Smart grids; Energy efficiency;
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:	15013
Identification Number:	https://doi.org/10.1016/j.apenergy.2019.02.020
Depositing User:	Fabiano Pallonetto
Date Deposited:	15 Nov 2021 14:49
Journal or Publication Title:	Applied Energy
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
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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