Machine Learning Could Make Geothermal Energy More Affordable | OilPrice.com
Machine Learning Could Make Geothermal Energy More Affordable OilPrice.com
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Machine Learning Could Make Geothermal Energy More Affordable
By Felicity Bradstock – May 12, 2024, 12:00 PM CDT
As governments
SDGs, Targets, and Indicators
1. Which SDGs are addressed or connected to the issues highlighted in the article?
- SDG 7: Affordable and Clean Energy
2. What specific targets under those SDGs can be identified based on the article’s content?
- Target 7.2: Increase substantially the share of renewable energy in the global energy mix
- Target 7.3: Double the global rate of improvement in energy efficiency
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
- Indicator 7.2.1: Renewable energy share in the total final energy consumption
- Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP
The article discusses how machine learning technology can make geothermal energy more accessible and affordable, which aligns with SDG 7: Affordable and Clean Energy. Specifically, the article highlights the potential of AI-powered drilling technology to reduce exploration costs and improve the efficiency of geothermal energy production.
Based on the content of the article, the targets under SDG 7 that can be identified are Target 7.2: Increase substantially the share of renewable energy in the global energy mix, and Target 7.3: Double the global rate of improvement in energy efficiency. These targets are relevant because geothermal energy is a renewable energy source, and advancements in AI and machine learning can contribute to improving energy efficiency in geothermal operations.
The article mentions Zanskar, a startup that uses machine learning models to analyze data and determine the best locations for geothermal drilling. This implies the use of Indicator 7.2.1: Renewable energy share in the total final energy consumption, as the technology aims to increase the share of geothermal energy in the global energy mix.
The article also mentions the U.S. National Renewable Energy Laboratory (NREL) and the U.S. Geothermal Technologies Office (GTO) developing AI and machine learning techniques to enhance geothermal energy production. This implies the use of Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP, as these advancements aim to improve energy efficiency in geothermal operations.
SDGs, Targets, and Indicators Table
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 7: Affordable and Clean Energy | Target 7.2: Increase substantially the share of renewable energy in the global energy mix | Indicator 7.2.1: Renewable energy share in the total final energy consumption |
| SDG 7: Affordable and Clean Energy | Target 7.3: Double the global rate of improvement in energy efficiency | Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP |
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Fuente: oilprice.com
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