Abstract
Large-scale adoption of solar photovoltaics (PV) in the built environment requires automation of roof suitability surveying over large geographical areas. Furthermore, as local PV installation density increases, electricity network operators require clearer information on the overall impact the large number of different rooftop PV systems will have on the stability of the local network. Knowledge of roof features (tilt angle, azimuth angle, area) and localised in-plane irradiance data is essential to meet both of these requirements. Such information is currently not available (except by individual roof surveying by PV consultants) and has to be generated. This paper demonstrates the automated extraction of building roof plane characteristics from existing wide-area, aircraft-based LiDAR (Light Detection and Ranging) data. These characteristics are then aggregated statistically and scaled-up to produce a UK-wide map of average roof tilt variation. Validation of roof tilt with site measurements taken by four different methods demonstrates a mean absolute error of 3 degrees. For major roof plane azimuth angles, banded into compass octants, accurate detection was achieved in 100% of cases, validated by inspection of aerial photography. This is sufficient for calculating in-plane irradiance for a more detailed automated assessment.
Highlights
Background to topicautomated extraction of 3D urban features is a challenging problem
The roof tilt of individual buildings calculated from geographic information system (GIS) weighted least squares fit were compared with values from a
Roof tilt was calculated by measurement of the apex. These results indicate that a light detection and ranging (LiDAR) resolution of at least 1 m is necessary for reliable roof tilt estimates
Summary
Knowledge of roof tilt and azimuth angles is necessary to calculate the electricity yield and generation time profile of an existing or potential photovoltaic (PV) installation. This mathematically generated data illustrates that more favourable (i.e. southerly) azimuths for PV installation may receive twice the amount of irradiation (for the same tilt) than the least favourable compass directions (i.e. east or west) for solar panels. A new method to identify these is presented here based on publically available data This will be essential for assessing domestic systems (or building added/integrated systems in general) and their effects on the infrastructure
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