| Item Type |
Conference or Workshop Item
(Paper)
|
| 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. |
| Authors |
Hanna, Joëlle; Mommert, Michael & Borth, Damian |
| Research Team |
AIML Lab |
| Language |
English |
| Subjects |
computer science |
| HSG Classification |
contribution to scientific community |
| Date |
13 October 2022 |
| Event Title |
AI4EO Symposium |
| Event Location |
Munich |
| Event Dates |
13-14 Oct. 2022 |
| Depositing User |
Joëlle Hanna
|
| Date Deposited |
06 Dec 2022 09:06 |
| Last Modified |
06 Dec 2022 09:06 |
| URI: |
https://www.alexandria.unisg.ch/publications/268266 |
Estimation of Power Generation and CO2 Emissions Using Satellite Imagery
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
