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An Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale
File(s)
File | Description | Size | Format | |
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2018_Usman_SimBuild_KnowledgeBase.pdf | 1.16 MB |
Date Issued
28 September 2018
Date Available
12T09:21:34Z September 2019
Abstract
Buildings play a significant role in driving the urban demand and supply of energy. Research conducted in the urban buildings sector indicates that there is a considerable potential to achieve significant reductions in energy consumption and greenhouse gas emissions. These reductions are possible through retrofitting existing buildings into more efficient and sustainable buildings. Building retrofitting poses a huge challenge for owners and city planners because they usually lack expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper proposes a new methodology based on machine learning algorithms to develop an intelligent knowledge-based recommendation system which has the ability to recommend energy retrofit measures. The proposed methodology is based on the following four steps: archetypes development, knowledge-base development, recommendation system development and building retrofitting or performance analysis. A case study of Irish buildings dataset shows that the proposed system can provide effective energy retrofits recommendation and improve building energy performance.
Sponsorship
Science Foundation Ireland
Other Sponsorship
ESIPP UCD
Type of Material
Conference Publication
Copyright (Published Version)
2018 ASHRAE
Language
English
Status of Item
Peer reviewed
Description
The 2018 Building Performance Analysis Conference and SimBuild, Chicago, Illinois, United States of America, 26-28 September 2018
ISSN
2574-6308
This item is made available under a Creative Commons License
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