InfraredSolarModules is a machine learning dataset that contains real-world imagery of different anomalies found in solar farms. This dataset can be used for machine learning research to gain efficiencies in the solar industry. Infrared imagery is not widely available to researchers. In order to combat the lack of publicly available data on infrared imagery of anomalies in solar PV, this project presents a novel, labeled dataset to facilitate research to solve problems well suited for machine learning that can have environmental impact.
The dataset consists of 20,000 infrared images that are 24 by 40 pixels each. There are 12 defined classes of solar modules presented in this paper with 11 classes of different anomalies and the remaining class being No-Anomaly (i.e. the null case).
| Class Name | Images | Description |
|---|---|---|
| Cell | 1,877 | Hot spot occurring with square geometry in single cell. |
| Cell-Multi | 1,288 | Hot spots occurring with square geometry in multiple cells. |
| Cracking | 941 | Module anomaly caused by cracking on module surface. |
| Hot-Spot | 251 | Hot spot on a thin film module. |
| Hot-Spot-Multi | 247 | Multiple hot spots on a thin film module. |
| Shadowing | 1056 | Sunlight obstructed by vegetation, man-made structures, or adjacent rows. |
| Diode | 1,499 | Activated bypass diode, typically 1/3 of module. |
| Diode-Multi | 175 | Multiple activated bypass diodes, typically affecting 2/3 of module. |
| Vegetation | 1,639 | Panels blocked by vegetation. |
| Soiling | 205 | Dirt, dust, or other debris on surface of module. |
| Offline-Module | 828 | Entire module is heated. |
| No-Anomaly | 10,000 | Nominal solar module. |
The file 2020-02-14_InfraredSolarModules.zip contains the images directory and module_metadata.json that describes each image. The JSON file is structured as follows:
{
"<image_number>": {
"image_filepath": "images/<image_number>.jpg",
"anomaly_class": "<class_name>"
},
...
}
This dataset was originally published at ICLR 2020 in AI for Earth Sciences workshop.
https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf