Solar Energy Dataset

This dataset provides a monthly compilation of climate variables for an entire year crucial for evaluating the feasibility of solar power projects, including solar irradiance, surface albedo, cloud cover, and temperature—making it highly relevant for environmental and energy research communities. The data can be used to train machine learning models that classify or rank geographic regions based on their suitability for solar energy harvesting, offering support to urban planners, energy developers, and policy-makers for data-driven decision-making. Researchers working in climate science, renewable energy planning, or geospatial analytics can leverage this dataset to assess energy potential and analyze regional variability, helping optimize site selection strategies in different parts of the world. The dataset facilitates the creation of interactive geospatial visualizations and maps, helping to democratize energy insights by making them accessible to a broader range of stakeholders including NGOs, startups, and governments focused on sustainable development. This data can be used to benchmark and evaluate machine learning models aimed at site selection, climate prediction, or grid expansion, while also serving as a base for transfer learning in broader energy or environmental prediction tasks. By offering publicly accessible, structured climate and solar energy data across a 10 -years period(January 2013 to December 2022) [1], this collection supports longitudinal studies and helps compare trends in solar resource availability across seasons and regions—an essential step toward scaling clean energy initiatives globally