Using LiDAR to assess rooftop solar photovoltaic potential on a large scale

From Appropedia
Jump to: navigation, search

Sunhusky.png Developed by Michigan Tech's Open Sustainability Technology Lab. For more see MOST's Appropedia Hub.

Wanted: Grad students interested in making a solar-powered open-source 3-D printing and distributed manufacturing future. Apply now.
Contact: Professor Joshua Pearce

Feedingeveryone.jpg

Lidar.png

Contents

[edit] The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential on a Municipal District Unit

Source: Ha T. Nguyen, Joshua M. Pearce, Rob Harrap, and Gerald Barber, “The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential on a Municipal District Unit”, Sensors, 12, pp. 4534-4558 (2012). Free open access

[edit] 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.

[edit] Application of LiDAR assessment of rooftop PV

[edit] See also