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SaurKshetra: A curated dataset and ML-based classification for solar energy site selection
Yash Shivgan1, Chandan Bhirud1, Anshul Jamgade1
1Vishwakarma Institute of Information Technology, Kondhwa Budruk, Pune, India.
Data in Brief
|June 15, 2026
Summary
India
Area of Science:
- Renewable Energy
- Geospatial Science
- Environmental Monitoring
Background:
- Growing global demand for clean energy necessitates identifying optimal solar power sites.
- India's vast geography offers significant solar energy potential, requiring accurate site selection data.
- Existing data gaps hinder the effective utilization of India's solar resources.
Purpose of the Study:
- To create a comprehensive geospatial dataset of environmental variables relevant to solar power generation in India.
- To develop a robust methodology for classifying solar power site suitability across India.
- To provide a free, ready-to-use resource for researchers and policymakers involved in solar energy development.
Main Methods:
- Acquisition of monthly environmental data (irradiance, albedo, cloud cover, temperature, etc.) from 371 Indian sites (2013-2022).
- Application of a weighted scoring method to rank site suitability.
- Utilisation of K-means clustering and Random Forest modeling for classification of solar power potential.
Main Results:
- A geospatial dataset covering 371 sites across five Indian regional zones (North, East, West, Central, South) is generated.
- Sites are categorized into five suitability classes (Excellent to Unsuitable) based on weighted environmental variables.
- Spatial maps and rankings detailing solar energy potential throughout India are produced.
Conclusions:
- The research provides a valuable, data-driven resource for identifying and prioritizing solar power sites in India.
- The developed methodology and dataset facilitate informed planning and policy decisions for renewable energy expansion.
- Open access availability of the data (DOI: 10.17632/hkcyvhvmwp.5) promotes further research and development in solar energy.
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