Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
|Title:||Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources||Authors:||Kathirgamanathan, Anjukan; De Rosa, Mattia; Mangina, Eleni; Finn, Donal||Permanent link:||http://hdl.handle.net/10197/11851||Date:||12-Aug-2020||Online since:||2021-01-19T10:52:50Z||Abstract:||Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.||Funding Details:||Science Foundation Ireland||Funding Details:||ESIPP UCD||Type of material:||Conference Publication||Keywords:||Smart grid; Building energy management; Building models; Energy systems||Other versions:||https://www.ashrae.org/conferences/topical-conferences/2020-building-performance-analysis-conference-simbuild||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The 2020 Building Performance Modeling Conderence and SimBuild, Chicago, United States of America, 12-14 August 2020||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Mechanical & Materials Engineering Research Collection|
Computer Science Research Collection
Energy Institute Research Collection
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