GIS-Based Estimation of Seasonal Solar Energy Potential for Parking Lots and Roads

TL;DR

This paper presents a GIS-based method to accurately estimate seasonal solar energy potential on urban surfaces, accounting for shadows cast by trees and buildings, including seasonal changes in deciduous trees.

Contribution

The paper introduces a novel approach using pixel substitution and light penetration factors to improve solar potential estimates in urban environments with seasonal foliage.

Findings

Method effectively models seasonal shadow variations.

Produces detailed solar maps for urban planning.

Applicable to parking and routing for solar vehicles.

Abstract

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.