Motivation Behind this Project

The world׳s energy demand is growing fast because of population growth and technological advancements. It is therefore important to look for a reliable, cost-effective, and renewable energy source to meet rising energy demand in the future. Solar energy, among other renewable sources of energy, is a cost-efficient energy source. Furthermore, solar energy is promising for environmental protection. Since it does not require burning fuels, solar energy is clean and free of greenhouse gas emissions, making it a good option for managing crises from climate change. The solar energy would definitely be an excellent option for meeting future energy demand since it is superior in the terms mentioned above compared to other energy sources. The solar energy industry is developing steadily throughout the world because of the high demand for energy while major energy sources such as fossil fuels are limited, and other alternative energy sources are expensive. However, Canada has a relatively small portion of Solar Capacity compared to other developed countries. According to the data from the Canada National Energy Board (NEB), only 0.5% of national electricity generation is from solar power. Thus, Canada has a huge potential to expand the solar industry, and it is therefore important to prepare strategies for promoting solar energy. The figure below shows Canada’s national solar energy map. This map is visual representations of Solar Production Potential, showing the most potential in the Canadian Prairies. Large municipalities near this region, such as Calgary, are good candidates for promoting residential solar panels.

Residential roof solar panels and solar farms are two options for utilizing solar energy. A centralized solar farm has advantages of maximizing the energy output. On the other hand, decentralized residential solar panels have several advantages. They require less land, provide energy independence to residences, and reduce burden on government budgets. For these reasons, it may be best to promote residential solar panels over centralized solar power plants.

In this project, a GIS approach was designed to analyze the rooftops in the City of Airdrie, a community north of Calgary, and determine suitable locations for installing solar panels. A suitability model for finding residences suitable for installation has was built considering multiple factors, such as solar potential, roof aspect and slope, and distance to main roads and power lines. The the model is applied to produce a suitability map identifies the optimal residences for developing solar energy. The detailed methods and results are discussed below.


Among mathematical methods that are developed for selecting the most preferable alternatives in recent decades, MCDM (Multi-Criteria Decision Making) techniques have the advantages of decision making under numerous decision criteria and multiple decision alternatives. The analytic hierarchy process (AHP), a kind of pairwise comparison method for determining relative weights for multiple factors, has been studied extensively for the last 20 years since it was developed by Saaty in 1980. AHP methods since became popular due to their wide applicability and ease of use. In the last 20 years, AHP methods have been adapted to many applications related to MCDM. In this project, the previous work of MCDM architecture and AHP methods are studied and then have been applied to produce the residential solar panel suitability map.

1. MCDM Architecture

As shown in the Figure below, the overall architecture of the MCDM has been shown for this project. As noted above, generating a suitability map of the best locations for building residential solar panels is the overall goal of the project. Attributes of “Has good solar potential”, “Aspect towards the South”, “Flat slope for easy installation”, “Close to main roads” and “Close to power lines” are used for decision making. The decision alternatives are all of the possible rooftops where solar panels can be installed.

All the data used for building the suitability model are open datasets provided by the City of Airdrie. The table below shows a detailed description of the datasets used.

Dataset Description
Elevation Raster dataset representing the digital elevation model of the area
Building Footprints Feature class representing the all building shapes over the area
Roads Feature class representing the linear road network for the City of Airdrie
Power Lines Feature class representing the linear power lines

1.1 Alternatives

Digital elevation model (DEM) and building footprints for the city were used for determining all the alternatives. The digital elevation models were derived from LiDAR data and were saved in ArcGIS as a floating-point raster image. The roof layer is obtained by overlapping between the DEM raster layer and the building footprints layer. Since not all rooftops are suitable or available for solar panel installation, the rooftops layer has been filtered by other attributes, such as environmentally restricted areas (e.g., wildlife reservations and protection areas), culturally restricted areas (e.g., military zones and cultural heritage areas) and other restrictions (e.g., areas where equipment is already been built). The result, shown in the figure below, is a map of all rooftops that are possible alternatives for new solar panel installations.

1.2 Attributes

All these datasets are reclassified so that they can be combined together. In order to do so, these datasets need to be represented with a common measurement scale, such as 1 to 10. By using the Reclassify tool, the values are reclassified into ten equal intervals and assigned weights. The weighted values are reversed for slope and distance because lower slopes and lower distances to roads and power lines are preferred for solar panel installation. Since the solar panels should be facing south to get higher solar radiation, the suitable south-facing aspects were assigned higher weights.

2. Using AHP Methods to Determine the Criteria Weights

For the AHP method, the weights are determined by normalizing the eigenvector associated with the maximum eigenvalue of a ratio matrix. The general steps of the AHP method are:

  1. Development of the Pairwise Comparison Matrix
  2. Computation of the Criterion Weights
  3. Estimation of the Consistency Index (CI) Value

First, the four evaluation criteria were compared in a pairwise manner to produce the pairwise comparison matrix displayed in the Table below. The values in each row indicate the importance of the criterion identified in the left-hand column relative to the corresponding criterion identified in each column header. For example, since getting high solar potential will be three times more important than the distance to the main roads when deciding whether the rooftops are good for building solar panels, a value of 3 was assigned to the ratio of “solar energy potential” to “the distance to main roads”.

Criteria High Solar Potentials Flat Slope Aspect toward South Close to Power Lines Close to Main Roads
High Solar Potentials 1 2 3 4 5
Flat Slope 1/2 1 2 3 4
Aspect toward South 1/3 1/2 1 2 2
Close to Power Lines 1/4 1/3 1/2 1 1
Close to Main Roads 1/5 1/4 1/2 1 1

After normalization and weight averaging, the weights for the five criteria are calculated. The detailed calculation steps are shown in the table below, where criteria for High Solar Potentials, Flat Slope, Aspect toward South, Close to Power Lines, and Close to Main Roads are represented as A, B, C, D, and E respectively. Three steps are represented as three different colours (green is the original pairwise weight, orange is the normalized value, and the average normalized weight (w) is purple).

Criteria A B C D E A B C D E w
A 1 2 3 4 5 0.44 0.49 0.43 0.36 0.38 0.42
B 1/2 1 2 3 4 0.22 0.24 0.29 0.27 0.31 0.27
C 1/3 1/2 1 2 2 0.15 0.12 0.14 0.18 0.15 0.15
D 1/4 1/3 1/2 1 1 0.11 0.08 0.07 0.09 0.08 0.09
E 1/5 1/4 1/2 1 1 0.09 0.06 0.07 0.09 0.08 0.08
SUM 2.28 4.08 7.00 11.00 13.00 1.00 1.00 1.00 1.00 1.00 1.00

3. Using SAW Method to Generate the Final Suitability Map 

After determining the weights of each attribute, an overall score must be calculated based on the criteria and the weights. The data for each attribute was previously reclassified using a linear transformation process so that all criteria are represented in equal intervals as values ranging from 1 to 10. After calculating all standardized scores from all the attributes, the overall score is calculated using the Simple Additive Weighting (SAW) method. The result of this is an overall score calculated for all alternatives. Locations with relatively high scores will be considered most suitable for building residential solar panels, while locations with low scores will not be suitable. The output from this process is used to generate the final suitability map.

Result and Discussion

A preliminary suitability map has been generated, showing optimal buildings where solar panels can be installed on the rooftops. Different levels of colors are used to present the suitability levels. The most suitable locations are represented in bright yellow, while unsuitable locations are represented in dark red and blue. It can be seen that the buildings with relatively large flat rooftop areas are determined to be more suitable by the model because these buildings will have more chances to have the good aspects and slope for solar panel installations.

The figure below shows the suitability results for small residences. Even though some small residences do not appear suitable at small scales, the rooftops of those residences still have surfaces that have relatively high suitability. Residences usually have roofs with different aspects, where one side faces towards the south and the other one faces towards the north. When viewed at larger scales, the south-facing sides with higher solar potential that are considered most suitable by the model can be distinguished from the north-facing sides that are not suitable.

The final step was to compute whether each residence has a large enough suitable area to install solar panels. The Con tool has been used for selecting the areas with the highest suitability level. Then, after converting the raster to polygons, the Select Layer by Attribute tool was used to filter the polygons by shape area. Only residences with an area larger than 20 m2 identified as suitable by the model will be considered for installing solar panels. The figure below shows the final map identifying all suitable residences.


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Open Data:

[27] High Resolution Digital Elevation Model (HRDEM) – CanElevation Series:

[28] City of Airdrie Open Data: