Evidence map›Paper›PMID 42293435›Full record

ArticleData in brief2026

SaurKshetra: A curated dataset and ML-based classification for solar energy site selection.

Yash Shivgan, Chandan Bhirud, Anshul Jamgade, Atharva Kulkarni, Rutuja Patil, Gagandeep Kaur, David Bassir, Ghanshyam G Tejani

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Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yash ShivganVishwakarma Institute of Information Technology, Kondhwa Budruk, Pune, India.
Chandan BhirudVishwakarma Institute of Information Technology, Kondhwa Budruk, Pune, India.
Anshul JamgadeVishwakarma Institute of Information Technology, Kondhwa Budruk, Pune, India.
Atharva KulkarniVishwakarma Institute of Information Technology, Kondhwa Budruk, Pune, India.
Rutuja PatilDepartment of Computer Engineering, MIT, Academy of Engineering, Alandi, Pune, India.
Gagandeep KaurDepartment of Computer Engineering, MPSTME, SVKM's NMIMS University, Maharashtra, India.
David BassirSmart Structural Health Monitoring and Control Laboratory, DGUT-CNAM, Dongguan University of Technology, China.
Ghanshyam G TejaniDepartment of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global demand for clean energy is on the rise and the best solar power sites among various regions need to be identified. India has the potential to become an important source for solar energy due to its vast and diverse geographical area, however, site selection based on accurate data is necessary for the effective utilization. The present research work has provided a geospatial dataset of monthly environmental variables-such as global horizontal irradiance, clear-sky irradiance, surface albedo, cloud cover, temperature at 2 m, dew point, and precipitation-acquired at 371 sites in India from January 2013 to December 2022. The data is divided into five regional zones-North, East, West, Central, and South- and is available in XLSX format for ease of mapping and analytical workflows. A weighted scoring method is used for the selected variables to sort the sunniest spots into five categories of suitability ranging from Excellent to Unsuitable. K-means clustering helps in categorizing locations based on their solar power potential, whereas a Random Forest model fine-tunes the classification into the designated suitability classes. The spatial maps and rankings generated, thus, provide a detailed summary of the solar energy potential throughout India. Besides, all datasets and classification outputs have been made available for free on Mendeley Data (DOI:

Indexed as

Geospatial analysisMachine learningRenewable energySite selectionSolar energy dataset

Identifiers

PMID42293435
PMCPMC13264117

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.