SolarFCD unifies RGB and thermal solar panel images from prior datasets into 4,435 samples across healthy, surface obstruction, structural fault, and electrical fault classes, with ResNet101V2 reaching 86.68% accuracy as the strongest baseline.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
AI data center electricity demand will reach 1% of global power use by 2030, with concentrated siting causing high power stress in specific regions like Oregon, Virginia, and Ireland.
citing papers explorer
-
SolarFCD: A Large-Scale Dataset and Benchmark for Solar Fault Classification in Photovoltaic Systems
SolarFCD unifies RGB and thermal solar panel images from prior datasets into 4,435 samples across healthy, surface obstruction, structural fault, and electrical fault classes, with ResNet101V2 reaching 86.68% accuracy as the strongest baseline.
-
Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand
AI data center electricity demand will reach 1% of global power use by 2030, with concentrated siting causing high power stress in specific regions like Oregon, Virginia, and Ireland.