Input Variable Selection for Thermal Load Predictive Models of Commercial Buildings

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Title: Input Variable Selection for Thermal Load Predictive Models of Commercial Buildings
Authors: Kapetanakis, Dimitrios-StavrosMangina, EleniFinn, Donal
Permanent link: http://hdl.handle.net/10197/11547
Date: 15-Feb-2017
Online since: 2020-09-08T14:04:16Z
Abstract: Forecasting of commercial building thermal loads can be achieved using data from Building Energy Management (BEM) systems. Experience in building load prediction using historical data has shown that data analysis is a key factor in order to produce accurate results. This paper examines the selection of appropriate input variables, for data-driven predictive models, from wider datasets obtained from BEM systems sensors, as well as from weather data. To address the lack of available complete datasets from actual commercial buildings BEM systems, detailed representation of reference buildings using EnergyPlus were implemented. Different types of commercial buildings in various climates are examined to investigate the existence of patterns in the selection of input variables. Data analysis of the simulated results is used to detect the correlation between thermal loads and possible input variables. The selection process is validated by comparing the performance of predictive models when the full or the pre-selected set of variables is introduced as inputs.
Funding Details: Irish Research Council
University College Dublin
metadata.dc.description.othersponsorship: United Technologies Research Centre (UTRC)
Type of material: Journal Article
Publisher: Elsevier
Journal: Energy and Buildings
Volume: 137
Start page: 13
End page: 26
Copyright (published version): 2016 Elsevier
Keywords: Building thermal loadsInput selectionPredictive modelArtificial neural networksSupport vector machineEnergy consumptionRegression modelsOffice buildingsCooling load
DOI: 10.1016/j.enbuild.2016.12.016
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection

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