SaurKshetra: A Dataset for Solar Farms Potential Site Mapping using Suitability parameters in India

This dataset is a structured, high-precision compilation of environmental and solar irradiance data sourced from NASA POWER, covering India's major geographic zones over the span of one year (January to December 2022). It includes seven critical parameters—such as solar radiation, temperature, cloud cover, albedo, and precipitation—essential for evaluating solar power generation potential. The primary goal of the dataset is to aid in the identification of optimal locations for solar energy infrastructure by applying geospatial and machine learning techniques. Carefully preprocessed for consistency and organized for ease of use, this dataset is not only useful for current solar site suitability analysis but also offers long-term value to researchers, urban planners, and policymakers. It supports advanced analytics like clustering, classification, and visualizations, and can serve as a foundation for predictive modeling, transfer learning, and sustainability-oriented decision-making in the field of renewable energy.

本数据集为结构化高精度环境与太阳辐照度数据合集，数据源自NASA POWER项目，覆盖印度主要地理区域，时间跨度为2022年全年（2022年1月至12月）。其包含七项关键参数，例如太阳辐射、气温、云量、反照率(albedo)与降水量等，这些参数对于评估太阳能发电潜力至关重要。本数据集的核心目标为，通过运用地理空间技术与机器学习技术，辅助识别太阳能基础设施的最优部署点位。本数据集经过严格预处理以保障数据一致性，并经结构化组织便于使用，不仅可用于当前的太阳能场址适宜性分析，还能为研究人员、城市规划者与政策制定者提供长期应用价值。其支持聚类、分类与可视化等高级分析任务，还可作为可再生能源领域预测建模、迁移学习(transfer learning)以及可持续导向决策制定的基础支撑。