A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings

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Title: A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings
Authors: Tardioli, GiovaniKerrigan, RuthOates, MikeO'Donnell, JamesFinn, Donal
Permanent link: http://hdl.handle.net/10197/11019
Date: 1-Aug-2017
Online since: 2019-08-21T09:37:06Z
Abstract: In this paper the traditional use of data-driven models (DDM) as forecasting tools is coupled with parametric simulation to create a building modelling framework for demand profiling of a large number of buildings of the same typology. Most studies to date utilising DDM have been conducted on single buildings, with less evidence of the role that DDM may have as a modelling technique for application at scale. The proposed methodology is based on the use of a simulation-based building energy modelling tool and a parametric simulator to create a large dataset consisting of 4096 different building model scenarios. Three DDM techniques are utilised; Support Vector Machines, Neural Networks and Generalised Linear Models, these are trained and tested using the generated simulation dataset. Results, at an hourly resolution, show that DDM approaches can correctly emulate the outputs of the building simulation software with mean absolute error ranging from 4 to 9 percent for different DDM algorithms.
Funding Details: European Commission
Type of material: Conference Publication
Copyright (published version): 2017 the Authors
Keywords: Data-driven models (DDM)Forecasting toolsEnergy modelling toolSupport Vector MachinesNeural NetworksGeneralised Linear Models
DOI: 10.26868/25222708.2017.464
Other versions: http://www.ibpsa.org/
Language: en
Status of Item: Not peer reviewed
Is part of: Barnaby, C.S., Wetter, M. (eds.). Building Simulation 2017
Conference Details: BS 2017: Conference of International Building Performance Simulation Association, San Francisco, USA, 7-9 August 2017
ISBN: 978-1-7750520-0-5
Appears in Collections:Mechanical & Materials Engineering Research Collection

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