Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction

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Title: Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction
Authors: Ali, UsmanShamsi, Mohammad HarisAlshehri, FawazMangina, EleniO'Donnell, James
Permanent link: http://hdl.handle.net/10197/12263
Date: 4-Sep-2019
Online since: 2021-06-21T11:27:28Z
Abstract: Energy Performance Certificates (EPC) provide an indication of buildings’ energy use. The creation of an EPC for individual building requires information surveys. Hence, these ratings are typically non-existent for entire building stock. This paper addresses these information gaps using machine-learning models. Developed models were evaluated with Irish EPC data that included approximately 650,000 residential buildings with 199 inputs variables. Results indicate that the deep learning algorithm produces results with highest accuracy level of 88% when only 82 input variables are available. This identified approach will allow stakeholders such as authorities, policy makers and urban-planners to determine the EPC rating for the rest of the building stock using limited data.
Funding Details: University College Dublin
Funding Details: ESIPP UCD
Type of material: Conference Publication
Publisher: IBPSA
Copyright (published version): 2019 the Authors
Keywords: Building energy performancePredictionMachine learning algorithms
DOI: 10.26868/25222708.2019.210232
Other versions: http://buildingsimulation2019.org/
Language: en
Status of Item: Peer reviewed
Is part of: Corrado, V. F,abrizio, E. Gasparella, A. and Patuzzi, F. (eds.). Building Simulation 2019
Conference Details: The 16th International Building Simulation Association, Rome, Italy, 2-4 September 2019
ISBN: 9781775052012
ISSN: 2522-2708
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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