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• W2911778082 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)." @default.
• W2911778082 created "2019-02-21" @default.
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• W2911778082 date "2019-04-01" @default.
• W2911778082 modified "2023-10-16" @default.
• W2911778082 title "Demand response algorithms for smart-grid ready residential buildings using machine learning models" @default.
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• W2911778082 doi "https://doi.org/10.1016/j.apenergy.2019.02.020" @default.
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