Employing Deep Learning to Quantify Power Plant Greenhouse Gas Emissions via Remote Sensing Data
Aryan Jain (Amador Valley High School)
Abstract
Greenhouse gasses (GHG) emitted from fossil-fuel-burning power plants pose a global threat to climate and public health. GHG emissions degrade air quality and increase the frequency of natural disasters five-fold, causing 8.7 million deaths per year. Quantifying GHG emissions is crucial for the success of the planet. However, current methods to track emissions cost upwards of $520,000/plant. These methods are cost prohibitive for developing countries, and are not globally standardized, leading to inaccurate emissions reports from nations and companies. I developed a low-cost solution via an end-to-end deep learning pipeline that utilizes observations of emitted smoke plumes in satellite imagery to provide an accurate, precise system for quantifying power plant GHG emissions by segmentation of power plant smoke plumes, classification of the plant fossil fuels, and algorithmic prediction of power generation and CO2 emissions. The pipeline was able to achieve a segmentation Intersection Over Union (IoU) score of 0.841, fuel classification accuracy of 92%, and quantify power generation and CO2 emission rates with R2 values of 0.852 and 0.824 respectively. The results of this work serve as a step toward the low-cost monitoring and detection of major sources of GHG emissions, helping limit their catastrophic impacts on climate and our planet.