Title:

Machine-Learning Methods and Tools Designed for Community-Based Equitable and Inclusive Geothermal Development

Authors:

Velimir (Monty) VESSELINOV, Hope JASPERSON, Tracy KLIPHUIS

Key Words:

machine learning, data analyses, artificial intelligence, community-based geothermal development

Conference:

Stanford Geothermal Workshop

Year:

2024

Session:

General

Language:

English

Paper Number:

Vesselinov

File Size:

2467 KB

View File:

Abstract:

To achieve net-zero carbon emissions, it is essential to involve communities in the implementation of green energy technologies. This can be done through informed decision-making, community-centered research, and engagement of stakeholders at the local, state, and regional levels. Community-led research and implementation are fundamental to achieving success. These collaborations should include rule makers, environmental regulators, clean energy industries, and technology researchers and developers. Unfortunately, many green infrastructure initiatives still adhere to a top-down and expert-driven process of site selection and design without awareness and acknowledgment of public engagement needs. This can lead to costly delays, including lawsuits, and ultimately less than desired or lacking outcomes as well as missed opportunities. Geothermal energy is a promising green energy source, but it is important to ensure that its development is equitable and does not disproportionately impact disadvantaged and underprivileged communities. To address this, we have developed a novel web-based interactive software and user-friendly interface called GeoTGO (https://geotgo.com) that provides everything that is needed for communities to better understand and develop their geothermal resources. Our website provides access to our machine-learning method and tools. Our tool also includes a comprehensive and living Community Engagement Plan (CEP) that was collaboratively developed with members of underprivileged and underrepresented communities. It bridges the gap between technology advancements and community needs by facilitating the interactions between the geothermal industry, regulators, stakeholders, and end-users. GeoTGO merges data, software (including data analysis, text mining, artificial intelligence, and modeling tools), knowledge, expertise, and experience to provide fast processing and dissemination of the latest information about cutting-edge geothermal technologies to users and communities. Machine learning and artificial intelligence methods in GeoTGO are based on our existing open-source algorithms (https://github.com/SmartTensors, https://github.com/madsjulia). GeoTGO is a valuable tool that can help communities to develop their geothermal resources in a way that is equitable and sustainable. We are currently working with several New Mexico Native Nations to pilot the tool and are planning to expand it to other communities in the future. GeoTGO will help to accelerate the development of geothermal energy and contribute to the achievement of net-zero carbon emissions.


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