Coro G., Trumpy E.
General Environmental Science Sustainability and the Environment Renewable energy Open science Geothermal energy Machine learning Industrial and Manufacturing Engineering Environment Strategy and Management Renewable Energy Artificial intelligence Spatial probability distributions
A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits.
Source: Journal of cleaner production (2020). doi:10.1016/j.jclepro.2020.121874
Publisher: Butterworth-Heinemann,, Oxford , Regno Unito
@article{oai:it.cnr:prodotti:422186, title = {Predicting geographical suitability of geothermal power plants}, author = {Coro G. and Trumpy E.}, publisher = {Butterworth-Heinemann,, Oxford , Regno Unito}, doi = {10.1016/j.jclepro.2020.121874}, journal = {Journal of cleaner production}, year = {2020} }