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

• Published in: Applied energy Vol. 239; pp. 1265 - 1282
• Main Authors: Pallonetto, Fabiano, De Rosa, Mattia, Milano, Federico, Finn, Donal P.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2019
• Subjects: Building demand response; Control algorithms; Energy efficiency; Machine learning; Optimisation; Smart grids; Energy efficiency; Building demand response; Smart grids; Optimisation; Machine learning; Control algorithms
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.02.020

•Co-simulation and building energy models provide a framework to test control algorithms.•Machine learning algorithms are effective to develop predictive optimisation models.•Predictive controls for residential can reduce heating systems costs up to 40%.•Smart predictive control in buildings results in lower carbon footprint up to 39%.•The smart grid dynamically controls dwellings to balance electricity supply/demand. 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).