• Energy Research
• other engineering and technologies close

AbstractThis 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.