Estimation of Power Generation and CO2 Emissions Using Satellite Imagery
Type
conference paper
Date Issued
2022-10-13
Author(s)
Research Team
AIML Lab
Abstract
Burning fossil fuels produces large amounts of carbon dioxide (CO2), a major Greenhouse Gas (GHG) and a main driver of Climate Change.
Quantification of GHG emissions related to power plants is crucial for accurate predictions of climate effects and for achieving a successful energy transition (from fossil-fuel to carbon-free energy). The reporting of such emissions is only required in some countries, resulting in insufficient global coverage. In this work, we propose an end-to-end method to predict power generation rates for fossil fuel power plants from satellite images based on which we estimate GHG emission rates. We present a multitask deep learning approach able to simultaneously predict: (i) the pixel-area covered by plumes from a single satellite image of a power plant, (ii) the type of fired fuel, and (iii) the power generation rate. To ensure
physically realistic predictions from our model we account for
environmental conditions. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted, using fuel-dependent conversion factors.
Quantification of GHG emissions related to power plants is crucial for accurate predictions of climate effects and for achieving a successful energy transition (from fossil-fuel to carbon-free energy). The reporting of such emissions is only required in some countries, resulting in insufficient global coverage. In this work, we propose an end-to-end method to predict power generation rates for fossil fuel power plants from satellite images based on which we estimate GHG emission rates. We present a multitask deep learning approach able to simultaneously predict: (i) the pixel-area covered by plumes from a single satellite image of a power plant, (ii) the type of fired fuel, and (iii) the power generation rate. To ensure
physically realistic predictions from our model we account for
environmental conditions. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted, using fuel-dependent conversion factors.
Language
English
HSG Classification
contribution to scientific community
Event Title
AI4EO Symposium
Event Location
Munich
Event Date
13-14 Oct. 2022
Subject(s)
Division(s)
Eprints ID
268266
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AI4EO_extended_abstract.pdf
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333.31 KB
Format
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