Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data

Yan Huang,Bailang Yu,Zuoqi Chen,Jianping Wu,Liang Chen,Feng Zhao,Weiqing Mao,Bin Wu,Junhan Wu

doi:10.3390/rs71215877

Abstract

Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning.

Highlights

Today’s global energy demand still relies largely on fossil fuels [1]
The estimation of the potential solar radiation on building roofs is significantly important for improving the efficiency of solar energy utilization in the urban environment
By integrating high-resolution airborne Light Detection and Ranging (LiDAR) data and FengYun meteorological data, we proposed a new Graphic Processing Unit (GPU)-based solar radiation model, named SHORTWAVE-C, which is updated from the SHORTWAVE model developed by Kumar, et al [8], to evaluate solar radiation intensity under cloudy condition

Summary

Introduction

Today’s global energy demand still relies largely on fossil fuels [1]. This results in rapid increase in anthropogenic greenhouse gas emissions and overexploitation of natural resources. The pursuit of new renewable energy, such as solar, wind, biomass, nuclear, and hydropower, as substitutions for traditional fossil fuels is an urgent need [2,3]. Common urban applications include the solar thermal collector for water heating and the solar photovoltaic panel placed on building roofs [6,7]. The estimation of the potential solar radiation on building roofs is significantly important for improving the efficiency of solar energy utilization in the urban environment

Objectives

Methods

Results