Solar Energy Dataset
Description
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 1-year period(January 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
Files
Steps to reproduce
This dataset comprises an extensive collection of solar irradiance and environmental parameter records, spanning a 10-year period (monthly data), sourced from NASA POWER Data Access Viewer. It focuses on crucial climate and geographic parameters essential for solar energy site suitability analysis, ensuring a comprehensive understanding of solar power potential. The dataset includes Seven key parameters critical to solar energy generation: 1.All Sky Surface Shortwave Downward Irradiance – Measures the total solar radiation received at the Earth's surface, influencing photovoltaic (PV) energy output. 2.Clear Sky Surface Shortwave Downward Irradiance – Represents the ideal solar radiation under cloud-free conditions, helping evaluate energy potential. 3.All Sky Surface Albedo – Indicates the reflectivity of the surface, affecting solar panel efficiency in different terrains. 4.Cloud Amount – Provides insights into cloud cover variations, crucial for understanding sunlight availability. 5.Temperature at 2 Meters – Affects solar panel efficiency, as extreme heat can reduce performance. 6.Dew/Frost Point at 2 Meters – Affects on the enfficiency 7.Precipitation Corrected Sum Each parameter is meticulously recorded and categorized, ensuring structured organization within the dataset. The dataset is formatted in CSV files, making it compatible with various machine learning models and geospatial analysis tools. Data points are recorded at a uniform monthly frequency, maintaining consistency in observation periods. To ensure high accuracy and usability, the dataset was preprocessed for missing values, outliers, and inconsistencies. The data is structured for easy integration into GIS platforms, statistical models, and AI-driven classification algorithms for solar site selection.
Institutions
- Vishwakarma Institute of Information Technology