Abstract
A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world.
Highlights
Solar photovoltaic (PV) energy conversion offers a sustainable method of producing electricity to provide for contemporary society’s needs [1]
The paper provides a methodology for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic deployment analysis on the regional scale
The methodology implements what previous literature recommends in terms of integrating cross disciplinary competences in remote sensing, geographical information systems (GIS), computer vision and urban environmental studies
Summary
Solar photovoltaic (PV) energy conversion offers a sustainable method of producing electricity to provide for contemporary society’s needs [1]. (iv) long–term economic growth improvement [4,5,6,7] for any country that aggressively develops the technology This has led to international cooperation and technology investment over the past 25 years, which in turn has given rise to fantastic gains in solar PV cell performance and a predicted changing landscape in R&D activities for solar cell technologies [8,9,10,11]. In the last decade (to 2010), global solar PV deployment has increased from 16 GW with an annual growth rate of more than 40% [12,13,14,15,16]. One approach is to take a LiDAR point cloud and transform it into a high-resolution and accurate 2.5D model of the scanned area. A Digital Surface Model (DSM) derived from point clouds acquired by
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