Demand response algorithms for smart-grid ready residential buildings using machine learning models

Fabiano Pallonetto, Mattia De Rosa, Federico Milano and Donal P. Finn

Applied Energy, 2019, vol. 239, issue C, 1265-1282

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).

Keywords: Building demand response; Optimisation; Machine learning; Control algorithms; Smart grids; Energy efficiency (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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DOI: 10.1016/j.apenergy.2019.02.020

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