Explore the data

Before you begin your analysis of solar power potential in Glover Park, you'll become familiar with the data and geography.

Open the project

First, you'll download and open the default project data in ArcGIS Pro.

  1. Download the Solar_in_Glover compressed folder.
  2. Locate the downloaded file on your computer.
    Note:

    Depending on your web browser, you may have been prompted to choose the file's location before you began the download. Most browsers download to your computer's Downloads folder by default.

  3. Right-click the file and extract it to a location you can easily find, such as your Documents folder.
  4. Open the Solar_in_Glover folder.

    The folder contains several subfolders, an ArcGIS Pro project file (.aprx), an ArcGIS Toolbox (.tbx), and two ArcGIS Pro layer files (.lyrx).

  5. If you have ArcGIS Pro installed on your machine, double-click the Solar_in_Glover project file. If prompted, sign in using your licensed ArcGIS account.
    Note:

    If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

    Default project

    The project contains two layers. The DSM layer, which is turned on by default, represents the neighborhood of Glover Park as a digital surface model (DSM).

    A DSM shows the elevation of the ground and features on the ground, such as buildings and trees. This DSM is a raster layer, which depicts data in a grid where each square, or cell, contains a numeric value.

  6. Below the map, in the scale bar, type 1:100 and press Enter.

    Scale bar set to 1:100

    The map zooms in so that you can see individual cells in the raster layer.

  7. Click any cell.

    The cell's pop-up appears.

    Pop-up for a raster cell

    The pop-up contains a value that represents the elevation (in meters) of the selected cell. In the example image, the highlighted cell has an elevation of about 89.9 meters.

    By default, the DSM is symbolized so that darker cells have lower elevations and lighter cells have higher ones.

  8. Close the pop-up. In the Contents pane, right-click DSM and choose Zoom To Layer.

    Zoom To Layer option for DSM layer

    The map navigates back to the full extent of the Glover Park neighborhood.

Create a hillshade effect

Although patterns in the DSM suggest the locations of structures and vegetation, you can better visualize the surface by creating a hillshade effect. A hillshade raster layer uses a realistic shading effect to depict elevation.

  1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click the Tools button.

    Tools button on Analysis tab

    The Geoprocessing pane appears.

  2. In the Geoprocessing pane search box, type Hillshade. In the list of results, click Hillshade (Spatial Analyst Tools).

    Search for Hillshade in the Geoprocessing pane

    The Hillshade tool opens.

  3. For Input raster, choose DSM. For Output raster, change the output name to Hillshade_DSM.
    Note:

    By default, all datasets you create will be saved in the Solar_in_Glover database. When you click the value in the Output raster parameter, the database path appears. Be sure to only change the name of the output layer, which occurs at the end of the path.

    Parameters for the Hillshade tool

    You'll leave the other parameters, which determine the direction and angle of the light source used to model shading, unchanged.

  4. Click Run.

    The tool runs and adds the hillshade layer to the map. However, the hillshade covers the original DSM, so you can't see the elevation information.

  5. In the Contents pane, drag the DSM layer above the Hillshade_DSM layer.

    Drawing order rearranged with DSM above Hillshade_DSM

    The DSM has 40 percent layer transparency. When it is drawn above the hillshade, both it and the hillshade are visible.

    Hillshade layer and transparent DSM on the map

  6. Zoom in until you can better see the 3D effect achieved by the hillshade layer.

    Hillshade effect displayed in more detail

    Buildings, trees, and other surface features are more distinguishable due to the hillshade effect. It's also possible to distinguish whether buildings have flat or gabled (sloped) roofs, which is important for rooftop solar panels.

  7. In the Contents pane, right-click DSM and choose Zoom to Layer.

Explore building footprints

The project contains a layer called Building_Footprints that is turned off by default. Next, you'll become familiar with this layer.

  1. In the Contents pane, check the check box for the Building_Footprints layer to turn it on.

    Building footprints on the map

    The layer contains the polygonal outline of every building in the Glover Park neighborhood. Unlike the DSM and hillshade layers, which are raster layers, this layer is a vector layer, which displays spatial information in the form of polygons, lines, or points.

  2. Zoom in until you can see individual building footprints more clearly.

    The way buildings are shaped and arranged is different in different areas of the neighborhood. In the north, west, and southeast, buildings tend to be larger and have more unique shapes. In the south central part of the neighborhood, buildings tend to be smaller, roughly rectangular, and bunched together in rows or columns.

  3. Return to the full extent of the neighborhood.
  4. On the Quick Access Toolbar, click the Save button.

    Save button on Quick Access Toolbar

    The project is saved.

You have downloaded, opened, and explored the initial data for the project, which includes elevation data and building footprints. You also created a hillshade to better visualize the neighborhood's surfaces.


Map solar energy

Next, you'll create a raster layer that maps how much solar energy reaches rooftop surfaces in Glover Park over the course of a typical year. The more solar energy a roof surface receives, the more electric power could be generated if it were equipped with solar panels.

Create a solar radiation layer

To create the solar radiation layer, you'll use the Area Solar Radiation tool. This tool is part of the ArcGIS Spatial Analyst extension. It calculates radiation based on a sophisticated model that takes into account the position of the sun throughout the year and at different times of day, obstacles that may block sunlight such as nearby trees or buildings, and the slope and orientation of the surface. You can read more about modeling solar radiation on the documentation page.

The DSM provides the necessary information on obstacles, orientation, and slope. The output will be a raster layer where each cell value is the amount of solar radiation in watt-hours per square meter (Wh/m2) at that location.

Note:

The Area Solar Radiation tool is computationally intense and can take between 20 and 90 minutes to run. For the purposes of this exercise, you'll have the option of running the tool yourself or using a solar radiation raster layer that has already been created.

  1. If necessary, open your Solar_in_Glover project in ArcGIS Pro.
  2. If necessary, open the Geoprocessing pane. If the Geoprocessing pane is already open to a previously used tool, click the Back button.

    Back button in Geoprocessing pane

  3. Search for and open the Area Solar Radiation tool.

    Search for Area Solar Radiation tool

  4. In the Area Solar Radiation tool, for Input raster, choose DSM. For Output global radiation raster, change the output name to Solar_Rad_Whm2_Example.

    When the input raster is chosen, the Latitude parameter is automatically populated with the latitude of the DSM. This parameter helps determine the sun's position.

    Input, output, and latitude parameters for the Area Solar Radiation tool

    By default, the tool will run using an interval of several days. You'll change the time interval so that solar radiation over the course of an entire year is calculated.

  5. For Time configuration, choose Whole year. If necessary, for Year, type 2019.

    By default, the tool computes the amount of solar radiation once every half hour for each day sampled. You'll change the hour interval to once every hour to reduce calculation time.

  6. For Hour interval, type 1.

    By default, the tool checks 32 directions around each cell to find light-blocking obstacles. To reduce calculation time, you'll change this value to 16.

  7. Expand Topographic parameters. For Calculation directions, type 16.

    Time and topographic parameters for the Area Solar Radiation tool

    You'll also change the tool environments so that only areas within the building footprints are processed, saving calculation time.

  8. Click the Environments tab. For Mask, choose Building_Footprints.

    Environments for the Area Solar Radiation tool

    The Area Solar Radiation tool is computationally intense. For the purposes of this exercise, you can choose to run the tool or use a solar radiation raster that has already been created and which was provided with the project data.

  9. If you want to run the tool (it may take 20 to 90 minutes to run), click Run. If you want to use the raster layer that was already created, open the Catalog pane and expand Databases and Solar_in_Glover.gdb. Add Solar_Rad_Whm2 to the map.
    Note:

    Depending on whether you create the layer yourself or add the existing layer to the map, the layer symbology may differ. You'll change the layer's symbology later in the lesson, so its current symbology does not matter.

    Default solar radiation layer on map

  10. Save the project.

Convert the units of measurement

The solar radiation raster uses watt-hours per square meter as its unit of measurement. According to the legend in the Contents pane, some cells have values of over 1 million (expressed with the notation e+06). To reduce the size of these values and make them easier to read, you'll convert the raster layer to kilowatt-hours per square meter (kWh/m2).

  1. In the Contents pane, uncheck Building_Footprints to turn it off.
  2. In the Geoprocessing pane, click the Back button (you may need to click it twice). Search for and open Raster Calculator (Spatial Analyst Tools).

    There are 1,000 watts in a kilowatt, so to convert the units of measurement, you only need to create an expression that divides existing cell values by 1,000.

  3. For Map Algebra expression, type (or build) the following expression:

    "Solar_Rad_Whm2" / 1000

    Note:

    If you created your own solar radiation raster layer, the name of the layer in the expression will be Solar_Rad_Whm2_Example instead of Solar_Rad_Whm2.

    Expression to convert watts to kilowatts

  4. For Output raster, change the output name to Solar_Rad.
  5. Click Run.

    The new raster layer is created and added to the map. It looks similar to the original solar radiation layer, but the values are 1,000 times smaller.

    You no longer need the original solar radiation layer, so you'll remove it.

  6. In the Contents pane, right-click Solar_Rad_Whm2 (or Solar_Rad_Whm2_Example) and choose Remove.

    Remove option for Solar_Rad_Whm2 layer

Symbolize the solar radiation layer

Next, you'll symbolize the Solar_Rad layer. For your analysis, you'll use uniform symbology for all solar radiation raster layers to ensure that they can be compared visually. You'll apply a layer file with predefined symbology to the layer. This layer file was included with the project data.

  1. In the Contents pane, click the Solar_Rad color ramp.

    Color ramp for the Solar_Rad layer

    The Symbology pane appears.

  2. In the Symbology pane, click the options button and choose Import.

    Import option for Solar_Rad symbology

  3. In the Import Symbology window, expand Folders and Solar_in_Glover. Double-click Solar_Rad.lyrx.

    The layer file is applied to the layer. The new symbology appears on the map.

    Symbolized solar radiation layer

  4. Close the Symbology pane.
  5. Zoom in to better see the rooftop surfaces.

    Symbolize solar radiation layer zoomed to buildings

    Red and orange colors indicate higher amounts of solar radiation, while yellow and blue tones indicate lower amounts. (Cells that are outside the Building_Footprints layer have a value of NoData and are not displayed.)

    North-facing roof slopes tend to receive less solar energy than south-facing ones. Additionally, roofs blocked by trees or other buildings sometimes receive less solar energy.

  6. Return to the full extent of the neighborhood. Save the project.

You've mapped annual solar energy on Glover Park rooftops. First, you created a solar radiation raster layer. Then, you converted the units of measurement and symbolized the layer for visualization purposes. Next, you'll identify rooftops suitable for solar panels.


Identify suitable rooftops

To identify suitable rooftops for solar panels, you'll consider three criteria:

  • Suitable rooftops should have a slope of 45 degrees or less, as steep slopes tend to receive less sunlight. To determine rooftop slope, you'll need to create a slope raster layer.
  • Suitable rooftops should receive at least 800 kWh/m2 of solar radiation. You can assess this criterion using your solar radiation raster layer.
  • Suitable rooftops should not face north, as north-facing rooftops in the northern hemisphere receive less sunlight. To determine rooftop orientation, you'll need to create an aspect raster layer.

Create a slope layer

First, you'll use the Slope tool to create a slope raster layer based on your DSM.

  1. If necessary, open your Solar_in_Glover project in ArcGIS Pro.
  2. If necessary, open the Geoprocessing pane. If the Geoprocessing pane is already open, click the Back button.
  3. Search for Slope and open Slope (Spatial Analyst Tools).
  4. For Input raster, choose DSM. For Output raster, change the output name to Slope_DSM.

    The other parameters, which determine how a slope is measured and calculated, do not need to be changed.

    Parameters for the Slope tool

  5. Click Run.

    The tool runs and the new raster layer is added to the map.

    Slope raster layer on map

    Each cell in this layer contains a slope value ranging from 0 to 90 degrees. The lighter colors represent lower slopes while darker colors represent steeper slopes.

Create an aspect layer

To determine the rooftop orientation, you'll create an aspect raster layer using the Aspect tool.

  1. In the Geoprocessing pane, click the Back button. Search for Aspect and open Aspect (Spatial Analyst Tools).
  2. For Input raster, choose DSM. For Output raster, change the output name to Aspect_DSM.

    You do not need to change the method by which the tool will calculate aspect.

    Parameters for the Aspect layer

  3. Click Run.

    The tool runs and the new raster is added to the map.

    Aspect raster layer on map

    Each cell contains a value expressing orientation in degrees, with 0 representing absolute north and 180 representing absolute south. The layer's legend lists the specific degree ranges for each direction.

Remove areas with high slopes

Next, you'll use your raster layers to find areas that meet the criteria for solar panels. First, you'll remove areas from your solar radiation raster layer that have a slope steeper than 45 degrees.

All of your raster layers use the same cell grid. Because of this, you can compare the values in the solar radiation and slope layers. You'll create an expression in the Con tool that checks whether each slope value is less than or equal to 45.

If a cell's slope is steeper than 45 degrees, its value will be changed to NoData in the output layer. Otherwise, the cell will be given its corresponding solar radiation value. The result will be a solar radiation raster layer that does not include slopes steeper than 45 degrees.

  1. In the Contents pane, uncheck the Aspect_DSM layer to turn it off.
  2. In the Geoprocessing pane, click the Back button. Search for Con and open Con (Spatial Analyst Tools).
  3. For Input conditional raster, choose Slope_DSM.
  4. For Expression, click New expression.

    New expression button in Con tool

  5. Create the expression Where VALUE is less than or equal to 45.

    Expression for the Con tool

    This expression will be applied to every cell in the slope raster. If a cell has a value less than or equal to 45, that cell is considered true. If not, the cell is considered false.

    Next, you'll choose the raster layer that will provide the output cell values for cells found to be true. Because your second criteria is solar radiation, you'll have the output layer use solar radiation cell values.

  6. For Input true raster or constant value, choose Solar_Rad.

    You also have the option to choose a raster layer or set a constant value for cells found to be false. You'll leave this parameter unchanged so that false cells are given the NoData value.

  7. For Output raster, change the output name to Solar_Rad_S.

    Parameters for the Con tool

  8. Click Run.

    The tool runs and the new raster is added to the map. Before you investigate the new layer, you'll change its symbology to match your solar radiation raster layer.

  9. In the Contents pane, click the Solar_Rad_S color ramp.

    The Symbology pane appears.

  10. In the Symbology pane, click the options button and choose Import.
  11. In the Import symbology window, open the Folders and Solar_in_Glover folders. Double-click Solar_Rad.lyrx.

    The new symbology is added to the layer.

  12. Close the Symbology pane. In the Contents pane, turn off the Slope_DSM and Solar_Rad layers.
  13. Zoom in to view buildings in more detail.

    Solar radiation raster layer with steep slopes removed

    Some locations on rooftops have been removed from the solar radiation layer. The removed areas are those with slopes above 45 degrees. Like the Solar_Rad layer, this layer depicts areas of higher solar radiation with darker red colors.

  14. Return to the full extent of the neighborhood. Save the project.

Remove areas with low solar radiation

Next, you'll consider the second criterion for suitable rooftops. Rooftop surfaces should receive at least 800 kWh/m2 in solar radiation if solar panels are to be installed. You'll use the Con tool on the Solar_Rad_S layer to remove areas with low solar radiation.

The Geoprocessing pane should already be open to the Con tool.

  1. In the Geoprocessing pane, for Input conditional raster, choose Solar_Rad_S.
  2. Remove the previous expression. Add a new expression that reads Where VALUE is greater than or equal to 800.
  3. For Input true raster or constant value, choose Solar_Rad_S. For Output raster, change the output name to Solar_Rad_S_HS.

    Parameters for the Con tool to determine high solar radiation

  4. Click Run.

    The new raster layer is added to the map. You'll symbolize it the same way you symbolized the other solar radiation layers.

  5. In the Contents pane, click the Solar_Rad_S_HS color ramp.

    The Symbology pane appears.

  6. In the Symbology pane, click the options button and choose Import. In the Import Symbology window, browse to Solar_in_Glover and double-click Solar_Rad.lyrx.

    The symbology is applied to the layer.

  7. Close the Symbology pane. Turn off the Solar_Rad_S layer.
  8. Zoom in to view buildings in more detail.

    Solar radiation raster layer with steep slopes and low radiation removed

    More unsuitable areas have been removed. These areas received low solar radiation, making them unideal for solar panels. The symbology of this layer is the same as your other solar radiation layers.

  9. Return to the full extent of the neighborhood. Save the project.

Remove areas that face north

The third criterion for suitable rooftops is that roof surfaces should not face north. In the northern hemisphere, surfaces facing north are likely to receive less solar radiation than surfaces facing other directions. (In the southern hemisphere, south-facing surfaces receive the least solar radiation.)

Many north-facing roof surfaces were already removed when you removed areas with low solar radiation, but some remain. Slopes that face north have a value less than 22.5 degrees or more than 337.5 degrees in the aspect raster layer. Additionally, you want to keep slopes that are almost flat, regardless of their aspect. If a roof is flat, its aspect doesn't matter for solar panels.

To fulfill both conditions, you'll use both Aspect_DSM and Slope_DSM. You'll run the Con tool twice, first to determine areas with low slopes (less than 10 degrees) and then to determine areas that face north.

  1. Confirm that the Geoprocessing pane is still open to the Con tool. For Input conditional raster, choose Slope_DSM.
  2. Remove the expression and add the new expression Where VALUE is less than or equal to 10.
  3. For Input true raster or constant value, choose Solar_Rad_S_HS. For Output raster, change the output name to Solar_Rad_Low_Slope.

    Parameters for the Con tool to determine low slopes

  4. Click Run.

    The new raster layer is added to the map. You'll run the Con tool a second time to determine north-facing surfaces.

  5. In the Con tool, for Input condition raster, choose Aspect_DSM.

    North-facing slopes are slopes that have a value less than 22.5 or more than 337.5. Your expression will require two clauses to fulfill both of these conditions.

  6. Remove the expression and add the new expression Where VALUE is greater than 22.5.
  7. Click Add Clause. Create the expression And VALUE is less than 337.5.

    Together, these clauses cover all surfaces that do not face north.

    You'll continue to use Solar_Rad_S_HS as the true raster, but you'll add the Solar_Rad_Low_Slope layer as the false raster. That way, false cells (those that face north) will be replaced by values from the low slope layer. The output layer will contain both areas that do not face north and areas with a low slope.

  8. For Input true raster or constant value, confirm that Solar_Rad_S_HS is chosen. For Input false raster or constant value, choose Solar_Rad_Low_Slope.
  9. For Output raster, change the output name to Solar_Rad_S_HS_NN.

    Parameters for the Con tool to remove north-facing slopes

  10. Click Run.

    The new raster layer is added to the map. You'll symbolize it the same way you've symbolized the other solar radiation layers.

  11. In the Contents pane, click the Solar_Rad_S_HS_NN color ramp. In the Symbology pane, click the options button and choose Import.
  12. Browse to the Solar_in_Glover folder and double-click Solar_Rad.lyrx.

    The symbology is added to the raster layer.

  13. Close the Symbology pane. Turn off the Solar_Rad_Low_Slope and Solar_Rad_S_HS layers.
  14. Zoom in to view buildings in more detail.

    Map with north-facing slopes removed

    Because many north-facing surfaces were removed when you removed areas with low solar radiation, the change between this layer and the previous one is not so dramatic. However, some areas did get removed, and this map now contains roof surfaces suitable for solar panels. For actual solar panel installation, it would be necessary to look at each rooftop in more detail, but for the purpose of your analysis, this approximation is sufficient.

  15. Return to the full extent of the neighborhood. Save the project.

You've analyzed your initial solar radiation layer by removing unsuitable areas for solar panels. With this suitable surface layer, you're ready to continue your analysis and aggregate solar radiation for each building.


Calculate power per building

Your map shows how much solar radiation each raster cell receives. The cells in your raster layers cover a relatively small area (0.5 square meters), so this information isn't that meaningful when looking at the entire neighborhood or even a single building.

In this lesson, you'll aggregate the solar radiation data to determine how much solar radiation each building receives in a typical year. Then, you'll convert the solar radiation to electric power production potential and examine your results.

Aggregate cells by building

First, you'll calculate the average solar radiation for each building using the Zonal Statistics as Table tool.

  1. If necessary, open your Solar_in_Glover project in ArcGIS Pro.
  2. Turn off the DSM and Hillshade_DSM layers. Turn on the Building_Footprints layer.

    Map showing building footprints and solar radiation

    By using the building footprints as boundaries, you can aggregate the solar radiation cells by building.

  3. If necessary, open the Geoprocessing pane.
  4. Search for and open the Zonal Statistics as Table tool.
  5. For Input raster or feature zone data, choose Building_Footprints. For Zone field, confirm that Building_ID is chosen.

    The Building_ID field is a unique identifier for each building footprint. Using this field as the zone field will ensure that each footprint is used for aggregation.

  6. For Input value raster, choose Solar_Rad_S_HS_NN. For Output table, change the output name to Solar_Rad_Table.

    You can choose to calculate several types of statistics. You'll calculate the mean to determine the average solar radiation per building.

  7. For Statistics type, choose Mean.

    Parameters for the Zonal Statistics as Table tool

  8. Click Run.

    The tool runs and the new table is added to the bottom of the Contents pane, under Standalone Tables.

    Tip:

    Your Contents pane contains many layers, so you may need to scroll to see the table. If you want to see all of your layers without needing to scroll, you can collapse the legends for the layers by clicking the arrow next to the layer name.

  9. In the Contents pane, right-click Solar_Rad_Table and choose Open.

    Open option for Solar_Rad_Table

    The table opens. It contains fields for the number of cells (COUNT), the area in square meters (AREA), and the average solar radiation in kWh/m2 (MEAN) for each building. The Building_ID field contains the building's unique identifier.

    Table of aggregated solar radiation information

    Because this table is stand-alone, it's not connected to the spatial data on your map. You'll join it to the Building_Footprints layer using the Add Join tool.

  10. Close the table. In the Contents pane, right-click Building_Footprints, point to Joins and Relates, and choose Add Join.

    The Geoprocessing pane appears, displaying the Add Join tool. This tool connects a table to a layer's attribute table or another table. For a join to work, there must be a matching field in both tables. In this case, the matching field is Building_ID.

  11. In the Add Join tool, for Layer Name or Table View, confirm that Building_Footprints is chosen. For Input Join Field, choose Building_ID.
    Note:

    When you choose the join field, you may receive a message that states the join field is not indexed. While this may affect performance, you can run the tool without indexing the field.

  12. For Join Table, confirm that Solar_Rad_Table is chosen. For Output Join Field, confirm that Building_ID is chosen.

    Parameters for the Add Join tool

  13. Click Run.

    The tool runs. The new table's data is joined to the Building_Footprints layer.

  14. In the Contents pane, right-click Building_Footprints and choose Attribute Table.

    The COUNT, AREA, and MEAN fields have been added to the end of the table.

  15. Close the table.

Find suitable buildings

You've determined suitable surfaces for solar panels. However, there is one more criterion to determine solar panel suitability. If a building has less than 30 square meters of suitable roof surface, it is generally not suitable for solar panel installation. You'll select buildings that have enough suitable roof surface using the Select Layer By Attribute tool.

  1. On the ribbon, click the Map tab. In the Selection group, click Select By Attributes.

    Select By Attributes button

    The Geoprocessing pane appears, displaying the Select Layer By Attribute tool.

  2. In the Select Layer By Attribute tool, for Input Rows, confirm that Building_Footprints is chosen. For Selection type, confirm that New selection is chosen.
  3. For Expression, click New expression. Create the expression Where AREA is greater than or equal to 30.

    Parameters for the Select Layer By Attribute tool

  4. Click Run.

    The selection is applied. Most buildings are selected. The exact number of selected features is listed below the map.

  5. Zoom in to view buildings in more detail.

    Selected buildings on map

    Many unsuitable buildings are particularly small. Others are larger but lack suitable surfaces for solar panels, possibly due to shade produced by nearby features.

  6. Return to the full extent of the neighborhood.

    You'll export the selected buildings into a new feature class.

  7. In the Contents pane, right-click Building_Footprints, point to Data, and choose Export Features.

    The Geoprocessing pane appears, displaying theFeatures Class to Feature Class tool.

  8. In the Features Class to Feature Class tool, for Input Features, confirm that Building_Footprints is chosen. For Output Location, confirm that Solar_in_Glover.gdb is chosen.
  9. For Output Feature Class, type Suitable_Buildings.

    Parameters for the Feature Class to Feature Class tool

  10. Click Run.

    The feature class is created and added to the map. You no longer need the original building footprints layer or the solar radiation stand-alone table, so you'll remove them.

  11. In the Contents pane, right-click Building_Footprints and choose Remove. Right-click Solar_Rad_Table and choose Remove.
  12. Save the project.

Create a field for solar radiation

Next, you'll create a field in the Suitable_Buildings attribute table. This field will contain the total amount of solar radiation received per year by each building's usable area. You'll calculate this field by multiplying each building's suitable area by its average solar radiation. To avoid the numbers becoming too large, you'll also convert the solar radiation from kilowatt-hours per square meter to megawatt-hours per square meter.

  1. In the Contents pane, right-click Suitable_Buildings and choose Attribute Table.
  2. On the ribbon of the attribute table, click the Add Field button.

    Add Field button

    The Fields view appears. In this view, you can edit existing fields or add new ones.

  3. On the bottom row of the Fields view, for Field Name, type Usable_SR_MWh. For Data Type, choose Double.

    You'll have the field round all values to 2 decimal places.

  4. For Number Format, double-click the empty cell and click the Determine display formatting for numeric field types button.

    Determine display formatting for numeric field types button

    The Number Format window appears.

  5. For Category, choose Numeric. Under Rounding, for Decimal places, type 2.

    Number Format window parameters

  6. Click OK.
  7. On the ribbon, in the Fields tab, in the Changes group, click Save.

    Save button

    The field is saved and added to the attribute table. Currently, its values are all null.

    New field with null values

    You'll calculate values for the field based on the values in the AREA and MEAN fields.

  8. In the attribute table, right-click the Usable_SR_MWh column name and choose Calculate Field.

    The Geoprocessing pane appears, displaying the Calculate Field tool. You'll create an expression that multiplies the area of suitable surfaces by the average solar radiation for each building. You'll divide the result by 1,000 to convert it from kilowatt-hours per square meter to megawatt-hours per square meter.

  9. In the Calculate Field tool, for Usable_SR_MWh =, create or copy and paste the following expression:

    (!AREA! * !MEAN!) / 1000

    Expression for the Calculate Field tool

  10. Click Run.

    The tool runs and the field is calculated.

    Calculated Usable_SR_MWh field

  11. Close the attribute table and the Fields view. Save the project.

    You now have an estimate of how much solar radiation every building receives each year on surfaces suitable for solar panels.

Convert solar radiation to power

Next, you'll convert the usable solar radiation values to electric power production potential. The amount of power that solar panels can produce depends not only on solar radiation, but also the solar panels' efficiency and the installation's performance ratio.

The United States Environmental Protection Agency (EPA) provides a conservative best estimate of 15 percent efficiency and 86 percent performance ratio. These values mean that the solar panels are capable of converting 15 percent of incoming solar energy into electricity, and 86 percent of that electricity is maintained throughout the installation.

To determine electric power production potential, you'll create a field and calculate it by multiplying your usable solar radiation values by the efficiency and performance ratio values.

  1. In the attribute table, click the Add Field button.
  2. In the Fields view, for the new field's Field Name, type Elec_Prod_MWh. For Data Type, choose Double.
  3. For Number Format, click the Determine display formatting for numeric field types button.
  4. In the Number Format window, for Category, choose Numeric. Under Rounding, for Decimal places, type 2.
  5. Click OK. On the ribbon, on the Fields tab, in the Changes group, click Save.

    The new field is added to the attribute table. Its values are null. Next, you'll calculate the field.

  6. In the attribute table, right-click the Elec_Prod_MWh column name and choose Calculate Field.
  7. In the Calculate Field tool, for Elec_Prod_MWh =, create or copy and paste the following expression:

    !Usable_SR_MWh! * 0.15 * 0.86

    Parameters for the Calculate Field tool for electric power

  8. Click Run.

    The tool runs and the field is calculated.

    Calculated Elec_Prod_MWh field

  9. Close the attribute table and the Fields view.

Symbolize the data

Your analysis is complete. Before you explore the results, you'll symbolize the layer based on the field you created. You'll also add a basemap for context.

  1. In the Contents pane, click the symbol for the Suitable_Buildings layer.

    The Symbology pane appears. It may be open to the symbol gallery, instead of the primary symbology page.

  2. If the symbol gallery is open, click the Back button.
  3. In the primary symbology page, click the options button and choose Import symbology.

    Import symbology option

    The Geoprocessing pane appears, displaying the Apply Symbology From Layer tool.

  4. In the Apply Symbology From Layer tool, for Symbology Layer, click the Browse button.

    Browse button

  5. In the Symbology Layer window, browse to the Solar_in_Glover folder and double-click Suitable_Buildings.lyrx.
  6. Leave the other parameters unchanged and click Run.

    The layer symbology is updated.

  7. On the ribbon, click the Map tab. In the Layer group, click Basemap and choose Dark Gray Canvas.

    Dark Gray Canvas basemap

    The basemap is added to the map.

    Final map

  8. Explore the final map.

    Larger buildings tend to have higher electric power production potential than single-unit dwellings. This pattern makes sense because larger buildings have larger roof surfaces. However, larger buildings also have higher electric power needs.

    The average household in the United States consumes 10.77 MWh per year. Could many households in the Glover Park neighborhood cover most or all their electric power needs with solar panels?

    You can also check the total amount of power that could be produced by the neighborhood.

  9. In the Contents pane, right-click Suitable_Buildings and choose Attribute Table.
  10. In the attribute table, right-click the Elec_Prod_MWh column name and choose Statistics.

    A chart opens, showing the distribution of the field values as a bar chart.

  11. On the ribbon of the chart, click the Properties button.

    Properties button

    The Chart Properties pane appears. The pane contains statistics, including the sum electric power production potential of all buildings.

    Note:

    Your statistics may differ slightly from the example image.

    Statistics with Sum field emphasized

    The entire neighborhood has the potential to produce over 20,000 MWh.

  12. Close the Chart Properties pane, the chart, and the attribute table. Save the project.

You've accomplished your goal and determined the solar power potential of the Glover Park neighborhood in Washington, D.C. To do so, you used a DSM to create a solar radiation raster layer. Then, you identified suitable rooftops for solar panels and calculated how much power these rooftops could generate.

Your results represent a yearly average estimate. However, solar-based electric power production varies according to the season, as day length and hours of sunlight change. You could also perform this lesson's workflow for specific days of the year, such as the winter and summer solstices and the fall and spring equinoxes, to determine the highest, lowest, and medium solar power production values.

This workflow can be replicated for any community, as long as you have building footprints and a DSM. Many communities provide open GIS data. The data for this lesson was acquired from the Open Data DC website.

You can find more lessons in the Learn ArcGIS Lesson Gallery.