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dc.contributor.advisorVan der Horst, Dan
dc.contributor.authorBalta, Chrysi
dc.date.accessioned2015-05-06T12:14:18Z
dc.date.available2015-05-06T12:14:18Z
dc.date.issued27/11/2014
dc.identifier.urihttp://hdl.handle.net/1842/10341
dc.description.abstractThis project aims to provide a methodology to map energy consumption of the housing stock at a city level and visualise and evaluate different retrofitting scenarios. It is based on an engineering, bottom-up approach. It makes use of the representative building typologies in the study area, according to the Hellenic and European standards, taking a step further by connecting it to the concept of GIS analysis. Firstly, the database of the housing stock of the area is created, to form an “energy cadastre”, by using statistical data and primary data derived from field surveys in the area. This enables the statistical analysis of the existing building stock and their characteristics. This cadastre is the basis on which to incorporate the data about energy consumption and energy class, which are derived from simulations of the typologies of the area and extrapolate them to the entire stock. Then, a review of policies and current trends in improving housing efficiency is conducted. Based on that, different scenarios are formed and simulated. The results are visualised and analysed on ESRI’s ArcMap. The differences between the scenarios are defined and the scenarios are evaluated for their effectiveness on the building stock. This project gives great attention to the relationship between energy consumption and spatial distribution. Clustering analysis is performed on the housing stock to reveal the connections and to identify areas of great need for retrofitting measures. This methodology can be adapted to every city and any national context, considering its special building characteristics and practices. It can be of great help to local authorities, which have a very important role in the implementation of energy policies. Not only it provides a way to analyse the existing stock and the energy consumption patterns, but also the means to massively classify the buildings in terms of energy performance and visualise the results of different retrofitting scenarios. With this approach, energy planners, local administrators and other stakeholders can take more effective actions at a city or neighbourhood level.en_US
dc.language.isoenen_US
dc.publisherThe University of Edinburghen_US
dc.subjectUrban energy policiesen_US
dc.subjectenergy consumptionen_US
dc.subjecthousing stocken_US
dc.subjectclustering analysisen_US
dc.subjectenergy performance classificationen_US
dc.subjectretrofitting scenariosen_US
dc.subjectGISen_US
dc.subjectUrban energy policiesen
dc.subjectenergy consumptionen
dc.subjecthousing stocken
dc.subjectclustering analysisen
dc.subjectenergy performance classificationen
dc.subjectretrofitting scenariosen
dc.subjectGISen
dc.subjectMSc Geographical Information Scienceen
dc.subjectGISen
dc.titleGIS-based energy consumption mappingen_US
dc.title.alternativeEstimating energy savings for the residential building stock at urban scaleen_US
dc.typeThesis or Dissertationen_US
dc.type.qualificationlevelMastersen_US
dc.type.qualificationnameMSc Master of Scienceen_US
dcterms.accessRightsRestricted Accessen_US


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