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 Kaggle
Cite 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
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Original source: The World Bank (2023). Visit official page for more details.
Indexed by IoTDataset.com on Feb 10, 2026
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