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Rooftop Solar Energy Potential Assessment Using GIS and Remote Sensing
- Jameer Sheik
- 7 minutes ago
- 7 min read
ABSTRACT
Urban areas offer significant potential for rooftop solar photovoltaic (PV) deployment due to the availability of unused roof spaces and high solar radiation levels. This study aims to assess the rooftop solar energy potential of Chromepet, Chennai using GIS-based spatial analysis and open-source geospatial datasets. Building footprint data obtained from OpenStreetMap and elevation data derived from Digital Elevation Model (DEM) were used to calculate rooftop areas, slope, and aspect parameters. Rooftop suitability was evaluated based on area, slope, and orientation criteria. Solar irradiance data were obtained from the NASA POWER database and integrated to estimate daily and annual energy generation potential. The results identify suitable rooftops and classify them into low, medium, and high solar potential categories. The findings demonstrate the effectiveness of GIS-based rooftop solar assessment for urban renewable energy planning and support sustainable energy development initiatives.
CHAPTER 1 — INTRODUCTION
1.1 Background of the Study
The rapid growth of urbanization and increasing electricity demand have intensified the need for sustainable and renewable energy solutions. Solar energy is one of the most promising renewable energy resources due to its abundance, environmental benefits, and decreasing installation costs. Rooftop solar photovoltaic systems are particularly suitable for urban environments as they utilize unused rooftop spaces and reduce dependency on conventional power sources.
Geographic Information Systems (GIS) and remote sensing technologies provide efficient tools for spatial analysis and decision-making in renewable energy planning. By integrating rooftop geometry, terrain characteristics, and solar radiation data, GIS enables accurate assessment of rooftop solar potential. This study applies GIS-based techniques to estimate rooftop solar energy potential in Chromepet, Chennai.
1.2 Problem Statement
Despite high solar radiation availability in Chennai, rooftop solar adoption remains limited due to lack of spatial information on suitable rooftops and energy generation potential. Manual rooftop assessment methods are time-consuming and inefficient. There is a need for a systematic GIS-based approach to identify suitable rooftops and quantify solar energy potential for urban planning and policy support.
1.3 Objectives of the Study
The main objectives of this study are:
To extract and analyse rooftop areas using OpenStreetMap building data.
To generate slope and aspect layers from DEM data.
To identify suitable rooftops based on spatial criteria.
To estimate daily and annual rooftop solar energy potential.
To classify rooftops into low, medium, and high solar potential zones.
To produce thematic maps for decision-making and urban energy planning.
1.4 Scope of the Study
This study focuses on rooftop solar photovoltaic potential assessment in Chromepet, Chennai. The analysis is limited to available building footprint data and medium-resolution DEM datasets. Shadow analysis using LiDAR and detailed rooftop structure modelling are not included. The results provide a preliminary estimation suitable for urban-level planning and policy formulation.
1.5 Study Area Description
Chromepet is a rapidly developing residential and commercial locality in South Chennai, Tamil Nadu. The area experiences a tropical climate with high solar insolation throughout the year. The dense urban built-up environment and flat rooftop structures make Chromepet suitable for rooftop solar energy assessment. The geographical location of the study area provides favourable solar radiation conditions for photovoltaic energy generation.
CHAPTER 2- LITERATURE REVIEW
Several studies have demonstrated the effectiveness of GIS-based methods for rooftop solar potential assessment. Researchers have used high-resolution satellite imagery, LiDAR data, and DEM-based terrain analysis to estimate rooftop area and orientation. Studies conducted in Indian cities highlight the importance of spatial data integration for urban renewable energy planning.
Previous works emphasize the role of slope, aspect, and shading analysis in rooftop solar suitability mapping. However, many studies face challenges due to limited availability of high-resolution elevation datasets. This study addresses these challenges by using openly available datasets and applying a simplified yet reliable methodology suitable for academic and planning applications.
Rooftop solar photovoltaic (PV) systems have gained significant attention as a sustainable solution for urban energy generation. Several researchers have applied Geographic Information Systems (GIS), remote sensing, and elevation data to assess rooftop solar potential in urban environments. The integration of spatial datasets such as building footprints, Digital Elevation Models (DEM), and solar radiation data has enabled efficient identification of suitable rooftops and estimation of energy generation capacity.
Kumar et al. (2020) conducted a GIS-based rooftop solar potential assessment in Chennai using highresolution satellite imagery and DEM data. The study extracted rooftop areas and evaluated solar suitability based on slope and aspect parameters. The results indicated substantial solar generation potential in dense residential and commercial zones, emphasizing the role of GIS tools in urban renewable energy planning.
Sharma and Singh (2021) analyzed rooftop solar feasibility in Delhi using LiDAR-derived elevation models and shadow analysis. Their research demonstrated that roof orientation significantly influences solar energy yield, with south-facing rooftops receiving maximum radiation in the northern hemisphere. The study highlighted the importance of terrain parameters such as slope and aspect in rooftop solar assessment.
Patel et al. (2019) evaluated rooftop solar potential in Ahmedabad using OpenStreetMap building data and medium-resolution DEM. The research showed that freely available geospatial datasets can provide reliable solar potential estimates at city scale. The study confirmed that GIS-based rooftop extraction combined with solar irradiance data offers a cost-effective method for renewable energy assessment.
Zhang et al. (2018) applied remote sensing and LiDAR techniques to estimate rooftop solar potential in Shanghai. Their methodology involved three-dimensional rooftop modelling and solar radiation simulation. The results demonstrated high accuracy in solar energy estimation and identified urban building density as a major factor influencing rooftop solar potential.
Similarly, Mainzer et al. (2014) developed a GIS-based approach for rooftop solar assessment across European cities using building geometry and solar radiation modelling. Their study confirmed that urban rooftops can contribute significantly to decentralized renewable energy production and reduce dependency on fossil fuels.
From the reviewed literature, it is evident that rooftop solar potential assessment commonly relies on rooftop area extraction, slope and aspect analysis, and solar radiation estimation. However, many studies depend on high-resolution LiDAR or detailed 3D building datasets, which are often unavailable for smaller urban regions. There is a need for methodologies that utilize freely available datasets such as OpenStreetMap building footprints and medium-resolution DEM for neighborhoodscale solar assessment.
Therefore, the present study applies a GIS-based rooftop solar potential assessment methodology using open-source building data, DEM-derived terrain parameters, and NASA POWER solar radiation data for Chromepet, Chennai. This approach provides a practical and cost-effective framework for urban rooftop solar planning in data-limited regions.
CHAPTER 3 — DATA AND SOFTWARE USED
3.1 Data Used
The datasets used in this study are summarized below:
3.2 Software Used
The following software tools were used:
ArcGIS Pro – Spatial data processing and analysis
NASA POWER Portal – Solar radiation data retrieval
Microsoft Excel – Data validation and calculations
CHAPTER 4 — METHODOLOGY
4.1 Overall Workflow
The methodology follows a step-by-step GIS-based workflow involving data preprocessing, rooftop extraction, terrain analysis, suitability filtering, and energy estimation.
4.2 Rooftop Data Processing
Building footprint data were obtained from OpenStreetMap and imported into ArcGIS Pro. The buildings layer was clipped to the Chromepet study area boundary. Geometry errors were corrected using the Repair Geometry tool. Rooftop area was calculated using the Calculate Geometry function and stored in square meters.
4.3 DEM Processing
The DEM was clipped to the study area boundary and pre-processed using the Fill tool to remove artificial sinks and surface irregularities. The filled DEM was used as the base input for terrain analysis.
4.4 Slope and Aspect Generation
Slope and aspect layers were generated from the filled DEM using Spatial Analyst surface tools. Slope represents surface inclination, while aspect represents surface orientation. These parameters are essential for evaluating rooftop solar suitability.
4.5 Rooftop Suitability Criteria
Rooftop suitability was evaluated using the following criteria:
Parameter | Threshold |
Roof Area | Greater than 20 m² |
Slope | Less than 20 degrees |
Aspect | 135° to 225° (South-facing) |
Buildings satisfying all criteria were classified as suitable rooftops for solar installation.
4.6 Solar Irradiance Integration
Monthly solar radiation data were obtained from NASA POWER using the parameter
ALLSKY_SFC_SW_DWN. The annual average irradiance value for Chromepet was calculated as 5.18 kWh/m²/day after unit conversion. This value was assigned to rooftop features for energy estimation.
4.7 Energy and Capacity Calculation
Daily rooftop solar energy generation was calculated using the formula:
Daily Energy = Roof Area × Solar Irradiance × Panel Efficiency × Performance Ratio
Panel efficiency was assumed as 18% and performance ratio as 0.75. Annual energy generation was calculated by multiplying daily energy by 365. Rooftop solar capacity was estimated using the standard assumption that 1 kW of solar PV requires approximately 6.5 m² of rooftop area.
CHAPTER 5 — RESULTS
5.1 Rooftop Area Analysis
The total rooftop area extracted from the building footprint dataset represents significant potential for solar PV deployment. Statistical analysis shows variation in rooftop sizes across residential and commercial structures.
PARAMETER | VALUE |
TOTAL BUILDINGS | 2755 |
SUITABLE ROOFTOPS | 742 |
TOTAL ROOFTOP AREA(m²) | 116939.7989 |
AVERAGE ROOFTOP AREA(m²) | 157.388693 |
5.2 Suitable Rooftop Distribution
Based on suitability criteria, rooftops were categorized into suitable and unsuitable groups. A large proportion of rooftops met the minimum area and orientation requirements.
PARAMETER | VALUE |
MINIMUM ANNUAL (kWh) | 6269.206064 |
MAXIMUM ANNUAL (kWh) | 285823.7547 |
AVERAGE ANNUAL (kWh) | 40172.59825 |
TOTAL ANNUAL ENERGY (kWh) | 29848240.5 |
5.3 Solar Energy Potential Results
The estimated annual rooftop solar energy generation potential ranges from approximately 6,200 kWh to 285,000 kWh per building depending on rooftop size and orientation. These values indicate strong potential for decentralized energy generation in Chromepet.
PARAMETER | VALUE |
MINIMUM CAPACITY (kW) | 3.778703 |
MAXIMUM CAPACITY (kW) | 172.277504 |
TOTAL CAPACITY (kW) | 17990.73831 |
5.4 Rooftop Capacity Results
The total installable rooftop solar capacity was calculated by aggregating individual building capacities. The results indicate significant scope for urban renewable energy expansion.
CLASS | ROOFTOPS | % |
LOW (< 51000 kWh) | 621 | 22.54083485 |
MEDIUM (51000-130000 kWh) | 91 | 3.303085299 |
HIGH (>130000 kWh) | 30 | 1.08892922 |
CHAPTER 6 — DISCUSSION
The results highlight Chromepet’s strong suitability for rooftop solar deployment. Larger commercial and institutional buildings contribute significantly to overall generation potential. The GIS-based methodology enables spatial identification of high potential zones, which can support urban planning and renewable energy policy implementation. The integration of open datasets demonstrates a costeffective approach suitable for city-scale solar assessment.
CHAPTER 7 — CONCLUSION
This study successfully demonstrated the use of GIS and remote sensing techniques for rooftop solar potential assessment in Chromepet, Chennai. Suitable rooftops were identified, and solar energy generation potential was estimated. The results confirm that Chromepet possesses considerable rooftop solar capacity that can contribute to sustainable urban energy development.