Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction
|Title:||Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction||Authors:||Ali, Usman; Shamsi, Mohammad Haris; Alshehri, Fawaz; Mangina, Eleni; O'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 performance; Prediction; Machine 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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