Solar radiation maps can help determine the suitability for generating energy from solar panels among other application. The increasing availability of high resolution LiDAR elevation data allows site specific assessment of solar potential considering elevation, slope and shade of surrounding features in conjunction with monthly averages of solar insolation (which is a measure of kilowatt hours per square metre). In this guide I explain how solar radiation maps were created for parts of the New Brunswick using new Natural Resources Canada data for municipalities. The resulting maps approximate the solar generation potential for rooftops in kilowatt-hours per square meter per day. These methods required a LiDAR derived Digital Surface Model and the solar radiation GIS model was calibrated using a history of solar radiation.
Getting started – Data Preparation
Software: ArcGIS was used with the Spatial Analyst Extension. The ESRI 3D Cities Template download includes a spreadsheet to organize and calibrate weather station data and geoprocessing models that assists with the processing.
Data: A LiDAR derived digital surface model (the elevation includes the tops of features such as buildings and trees), Global Horizontal Irradiance data, and building footprints are the three datasets required to replicate these methods. Here is a description of how each was prepared:
- A 1 metre resolution LiDAR derived digital surface model (DSM). The DSM was derived using first return elevations from LAS files. From the LiDAR DSM slope and aspect of rooftops as well as trees or other tall features that may cast shade can be derived.
- Natural Resources Canada provides Global Horizontal Irradiance (GHI) data for Canadian municipalities. The data can be downloaded from this link. The “municip_kWh.CSV” spreadsheet contains the GHI values in kWh per square metre. The data in the final column (Horizontal mean daily global insolation) was used for this analysis. If data is required for unincorporated areas, GHI values can be interpolated based on surrounding municipalities. It is worth taking the time to use this data rather than accepting the GIS model defaults. The GHI records provide seasonal atmospheric factors that influence solar radiation such as cloud cover and fog that are specific to the local climate.
- Building footprints were used to summarize the solar radiation data. Building footprints were manually digitized using the LiDAR DSM and orthophotos.
Methods: The methods used generally follow those outlined here, but were modified to use LiDAR derived rather than vector rooftop geometries. Please refer to the instructions found here for more detailed steps.
- Download and extract the 3D Cities Template. Add the 3DCitySolarTools toolbox in ArcGIS.
- Create a new layer with a single point at the geometric centre of each municipalities DTM. Extract the lat/long for each point. Run the Atmospheric Calibration tool in the 3DCitySolarTools toolbox using those coordinates and the LiDAR DSM.
- Open the DBF file associated with the Atmospheric Calibration tool output in a spreedsheet software such as Excel. Copy the data into the Best_D-T_Computations.xlsx. In this spreadsheet also add lat/long and the monthly GHI data. The spreadsheet will calculate the “Best Result” and these will be used when running the solar radiation model.
- Run the Solar Radiation Model as provided by the 3D Cities Template, using the “Best Results” values found in the previous step. I modified the model to save the monthly solar radiation raster layers, otherwise the only saved output will be the average annual solar radiation.
- The solar radiation data can then be summarized by building footprint using the Tabulate Area tool. Below is a screenshot of the solar radiation as the basemap, and the labes give the average annual kWh per square metre per day for each rooftop.