Abstract
"High-resolution, long-term historical data for solar photovoltaic (PV) and concentrated solar power (CSP) potential assessment anywhere on the globe."
Description
This dataset is a premier resource for solar energy prospecting and planning. It provides critical parameters derived from satellite and atmospheric data over a 22-year period, essential for feasibility studies and smart grid design.
Core Data Layers:
- Solar Irradiance: Includes Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI).
- PV Output: Simulated photovoltaic power output for various panel technologies and configurations.
- Spatial & Temporal Resolution: Data is available as monthly and yearly averages, as well as long-term typical meteorological year (TMY) time series at a high spatial resolution (~1 km).
- Use Case for IoT/AI: Serves as the foundational 'expected potential' data layer against which real-time production data from IoT-connected solar farms can be compared for performance monitoring, anomaly detection, and predictive maintenance.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleCite This Dataset
World Bank Group, & SOLARGIS (2023). High-Resolution Global Solar Atlas (SolarGIS). [Dataset]. The World Bank. https://doi.org/10.1163/9789004322712_cclc_2021-0024-004
Source: The World Bank (2023) · DOI: 10.1163/9789004322712_cclc_2021-0024-004
Indexed by IoTDataset.com on Feb 10, 2026
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