Automatised and georeferenced energy assessment of an Antwerp district based on cadastral data
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2018-08-15
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Abstract
Municipalities play a key role in supporting Europe's energy transition towards a low-carbon economy. However, there is a lack of tools to allow municipalities to easily formulate a detailed energy vision for their city. Nevertheless, most municipalities have access to georeferenced cartographic and cadastre information, including that on basic building characteristics. This article describes an innovative method to calculate and display the current hourly thermal energy demand for each building in a district based on basic cartography, cadastre, and degree-day values. The method is divided into two main blocks: (1) input data processing to obtain geometric information (e.g. geolocation, building and facades’ dimensions) and semantic data (e.g. use, year of construction), and (2) district energy assessment to calculate the thermal energy demand using data obtained in block 1. The proposed method has been applied and tested in the historical district of Antwerp. The reliability and thoroughness of the results obtained using the method are demonstrated based on two different validations: (1) comparison of the results with those calculated using an existing dynamic energy simulation tool, and (2) comparison of the results with the real gas consumption of a partial sector of the selected district. The first validation shows that the average difference between the two methodologies is less than 11% for the heating demand, less than 11% for the cooling demand, and less than 15% for the domestic hot water demand. The second validation shows a 24% difference between the real natural gas consumption and that obtained by new methodology. Finally, the results have been presented to the municipality of Antwerp, which plans to use the method to design the district heating expansion within the city centre. Furthermore, sensitivity assessment was used to determine the relevance of the main input parameters considered in this method, such as the base temperature, energy system schedules, window-to-wall ratio, and solar gains.
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Publisher Copyright: © 2018 The Authors
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District energy mapping , Energy demand , Cadastral data , Data process , Inference rules , Influence coefficient , Sensitivity assessment , District energy mapping , Energy demand , Cadastral data , Data process , Inference rules , Influence coefficient , Sensitivity assessment , Civil and Structural Engineering , Building and Construction , Mechanical Engineering , Electrical and Electronic Engineering , SDG 7 - Affordable and Clean Energy , SDG 8 - Decent Work and Economic Growth , Project ID , info:eu-repo/grantAgreement/EC/H2020/723757/EU/Integrated tool for empowering public authorities in the development of sustainable plans for low carbon heating and cooling/PLANHEAT , info:eu-repo/grantAgreement/EC/H2020/723757/EU/Integrated tool for empowering public authorities in the development of sustainable plans for low carbon heating and cooling/PLANHEAT , Funding Info , The work described in this article is partially funded by the PLANHEAT project, Grant_x000D_ Agreement Number 723757, 2016-2019, as part of the call H2020-EE-2016-RIA-IA. The field_x000D_ work for this study was conducted thanks to the active collaboration of the Energy and_x000D_ Environment department of the municipality of Antwerp. , The work described in this article is partially funded by the PLANHEAT project, Grant_x000D_ Agreement Number 723757, 2016-2019, as part of the call H2020-EE-2016-RIA-IA. The field_x000D_ work for this study was conducted thanks to the active collaboration of the Energy and_x000D_ Environment department of the municipality of Antwerp.
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Oregi , X , Hermoso , N , Prieto , I , Izkara , J L , Mabe , L & Sismanidis , P 2018 , ' Automatised and georeferenced energy assessment of an Antwerp district based on cadastral data ' , Energy and Buildings , vol. 173 , pp. 176-194 . https://doi.org/10.1016/j.enbuild.2018.05.018