Geometry-aware estimation of photovoltaic energy from aerial LiDAR point clouds

Aerial LiDAR (and photogrammetric) surveys are becoming a common practice in land and urban management, and aerial point clouds (or the reconstructed surfaces) are increasingly used as digital representations of natural and built structures for the monitoring and simulation of urban processes or the generation of what-if scenarios. The geometric analysis of a “digital twin” of the built environment can contribute to provide quantitative evidence to support urban policies like planning of interventions and incentives for the transition to renewable energy. In this work, we present a geometry-based approach to efficiently and accurately estimate the photovoltaic (PV) energy produced by urban roofs. The method combines a primitive fitting technique for detecting and characterizing building roof components from aerial LiDAR data with an optimization strategy to determine the maximum number and optimal placement of PV modules on each roof surface. The energy production of the PV system on each building over a specified time period (e.g., one year) is estimated based on the solar radiation received by each PV module and the shadow projected by neighboring buildings or trees and efficiency requirements. The strength of the proposed approach is its ability to combine computational techniques, domain expertise, and heterogeneous data into a logical and automated workflow, whose effectiveness is evaluated and tested on a large-scale, real-world urban areas with complex morphology in Italy.