Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources

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Title: Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
Authors: Kathirgamanathan, AnjukanDe Rosa, MattiaMangina, EleniFinn, 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 gridBuilding energy managementBuilding modelsEnergy 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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