An Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale

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Title: An Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale
Authors: Ali, UsmanShamsi, Mohammad HarisHoare, CathalMangina, EleniO'Donnell, James
Permanent link: http://hdl.handle.net/10197/11080
Date: 28-Sep-2018
Online since: 2019-09-12T09:21:34Z
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Copyright (published version): 2018 ASHRAE
Keywords: Built environmentEnergy demandBuilding energy retrofitRecommendation systems
Other versions: https://www.ibpsa.us/2018-building-performance-analysis-conference-and-simbuild-co-organized-ashrae-and-ibpsa-usa
Language: en
Status of Item: Peer reviewed
Conference Details: The 2018 Building Performance Analysis Conference and SimBuild, Chicago, Illinois, United States of America, 26-28 September 2018
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Energy Institute Research Collection

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