Abstract
This paper explores the potential for using remotely sensed data from a combination of commercial and open-sources, to improve the functionality, accuracy of energy-use calculations and visualisation of carbon emissions. We present a study demonstrating the use of LiDAR (Light Detection And Ranging) data and aerial imagery for a mixed-use inner urban area within the North East of England and how this can improve the quality of input data for modelling standardised energy uses and carbon emissions. We explore the scope of possible input data for both (1) building geometry and (2) building physics models from these sources.We explain the significance of improved data accuracy for the assessment of heat-loss parameters, orientation, and shading and renewable energy micro-generation. We also highlight the limitations around the sole use of remotely sensed data and how these concerns can be partially addressed through combinations with (1) open-source property data, such as age, occupancy, tenure and (2) existing stakeholder data sets, including building services and measured performance. We set out some of the technical challenges; addressed through data approximation (considering data quality and metadata protocols) and a combination of automated or manual processing; in the use, adaptation, and transferability of this data. We elucidate how the output can be visualised and be supported by many of industry-standard CAD, GIS, and BIM software applications hence, broadening the scope for real-world applications. We demonstrate the support of commercial interest and potential drawing evidence from primary market research regarding the principal applications, functionality, and output.In summary, we conclude on the benefits in the use of remotely sensed data for improved accuracy in energy use and carbon emission calculations, the need for semantic integration of mixed data sources and the importance of output visualisation.
Highlights
Introduction to Light detection and ranging (LiDAR)LiDAR is an active remote sensing technology
Community Domestic Energy Model (Firth et al 2010). All these models have the same energy calculation engine which is Building research establishment domestic energy model (BREDEM) (Building Research Establishment Domestic Energy Model) and the Standard Assessment Procedure (SAP) which is recommended by the Department Of Business Energy And Industrial Strategy (BEIS) in the UK as the main tool to underpin BREDEM for assessing and comparing energy performance of dwellings
This paper addresses the issue of raw data availability and accuracy through the development of new processes and techniques for data collection and in particular the automated process of capturing dimensions and footprint of dwellings through the combination of OSL (Ordinance Survey and Landmap) data and deployment of LiDAR and remote sensing as means for aerial and terrestrial imagery
Summary
Introduction to LiDARLiDAR is an active remote sensing technology. It allows acquiring topographical information over surfaces at high Level of Detail (LoD), for large-scale urban areas. This paper addresses the issue of raw data availability and accuracy through the development of new processes and techniques for data collection and in particular the automated process of capturing dimensions and footprint of dwellings through the combination of OSL (Ordinance Survey and Landmap) data and deployment of LiDAR and remote sensing as means for aerial and terrestrial imagery This captured geometrical data is further integrated with opensource and publically available data for a faster and more accurate energy calculations integrating data from available statistical sources, such as census data, deprivation and neighbourhood statistics data from ONS (Office of National Statistics), HEED (Homes Energy Efficiency Database) and EHS (English Housing Survey). The remainder of this paper discuss the main technique used to capture data, dealing with errors and data cleaning, integration with other data bases and initial results from a case study
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