GIS-Based Estimation of Seasonal Solar Energy Potential for Parking Lots and Roads
This research provides a useful resource for urban planners and solar vehicle designers by offering more accurate solar potential maps for parking and driving conditions, addressing a limitation in existing GIS tools.
This paper addresses the problem of estimating seasonal solar energy potential on urban surfaces, specifically parking lots and roads, by accounting for shadows from buildings and seasonal changes in deciduous trees. The authors introduce a new approach using pixel substitution and a light penetration factor to integrate these factors into a GIS-based workflow, demonstrating its application in an urban setting in North Carolina.
The amount of sun cast on roads and parking lots determines the charging opportunities for solar vehicles and impacts the efficiency of conventional vehicles. Estimates of solar energy potential on urban surfaces to assess parking and driving conditions need to account for the shadows cast by surrounding trees and buildings. However, though existing GIS tools can calculate solar potential on surfaces that have buildings and trees, these tools do not estimate the conditions beneath trees and do not consider the seasonal changes in deciduous trees. We introduce a new approach to address these factors using pixel substitution and a light penetration factor. In this paper, we describe how to integrate these techniques into a workflow for computing solar potential estimates for parking and driving conditions. We demonstrate the methodology in an urban setting in North Carolina that includes a mixture of urban structures and trees. We provide code samples so that this workflow is easily repeatable. The solar maps produced with our method are a useful resource for planning solar vehicle parking and routing, and identifying shaded conditions for conventional vehicles.