Palantir, TES Leverage AI to Advance Energy Transition, Decarbonization

Tree Energy Solutions (TES) and Palantir Technologies entered a multi-year partnership to use Palantir’s AIP software to accelerate the green energy transition, global decarbonization, and TES’ electric natural gas (e-NG) production. AIP incorporates large-language models and AI in enterprise networks, private data, and core operations.

"This collaboration with Palantir underscores TES' commitment to innovation, demonstrating how AI can accelerate the energy transition,” said Marco Alverá, CEO and Co-founder of TES. “By leveraging Palantir's AI expertise, we will optimize operations and track carbon emissions precisely, reinforcing TES as an innovative leader in the global green energy landscape."

Palantir Foundry and AIP will complement TES’ supply chain management, simulation, and scenario modeling for investment optimization, asset management, emissions tracking, and site selection. It combines siloed and disconnected data sources, logic assets, and systems of action, allowing organizations to make decisions through a common operating picture.

Features available in AIP; Image Credits: Palantir

“Palantir prides itself in helping organizations of all types solve their most difficult problems,” said Francois Bohuon, Commercial Leadership EMEA of Palantir Technologies. “Working with a green energy player like TES was the perfect fit, and we are honored to supply them with an enterprise AI system in support of their mission.”

e-NG is a green molecule identical to natural gas, produced by combining green hydrogen with biogenic or recycled CO₂. It can be transported and stored in existing infrastructure. TES formed partnerships to develop large-scale e-NG projects with TotalEnergies, Tokyo Gas, ADNOC, Fortescue, and more.

AI-integrated software, like Palantir’s AIP, is often used to monitor and record emissions data for CO₂ reduction. In addition to AI, it typically features IoT to enhance efficiency and accuracy in data reporting.

A similar platform, Siemens Energy’s Predictive Emissions Monitoring System (PEMS), enables operators to make informed decisions and develop strategies to cut back on carbon output. PEMS can predict exhaust emissions with internal system engine parameters, as well as use gas turbine data to create an algorithm that generates precise emissions signatures. It’s highly adaptable, allowing customers in the energy industry to view the emissions footprint for NOx, CO, and CO₂, and PEMS additionally reduces capital and operational expenditures.