> ## Content Index > Fetch the complete content index at: https://blog.predicterra.com/llms.txt > Use this file to discover other available public pages before exploring further. # Predicterra at Enlit Europe 2026 - URL: https://blog.predicterra.com/predicterra-at-enlit-europe-2026/ - Published: 2026-10-18T16:18:40.000Z - Updated: 2026-10-18T16:18:40.000Z - Author: Predicterra - Tags: News ### Three launches for the energy system's digital twin. Spain Pavilion, Messe Wien, Vienna, November 10 to 12 From **November 10 to 12**, Predicterra will be in Vienna for **Enlit Europe 2026**, as part of the **Spain Pavilion** organised by ICEX España. Enlit is where Europe's energy system meets: utilities, grid operators, technology providers, regulators and investors, more than 15,000 professionals over three days. This year's theme is **Integration. Resilience. Competitiveness.** It could have been written for what we build. Energy operators do not lack data. They lack a single model that integrates it, tells them what will happen to every asset, and shows them what to do first. That is the onto-physical digital twin: one model that fuses physics, assets and every operational system through a proprietary ontology. It is already running at UFD (Naturgy), recently published by E.DSO as an official success case, and builds on our work with i-DE (Iberdrola) on grid resilience. In Vienna, we will show what comes next. ## Three launches at Enlit ### 1\. A foundation model for grid incidents Every distribution operator holds years of incident records: faults, outages, interruptions, each one tied to an asset, a place, a moment and the weather that came with it. Today that history is mostly used for reporting. We think it is the most valuable training data in the sector. Predicterra's **incident foundation model** is pretrained on large volumes of historical incidents, fused with the meteorological and environmental conditions under which they occurred and the characteristics of the assets that failed. Instead of learning one network, it learns how networks fail: which combinations of wind, heat, rain, vegetation, age and load lead to which failures, and with what lag. The practical consequence is speed and transfer. A model that already understands failure dynamics can be adapted to a new operator, a new region or a new hazard with a fraction of the local data, and reach useful accuracy in weeks instead of seasons. Each new deployment, in turn, makes the base model better. ### 2\. The physical energy ontology Everything in Predicterra runs on one structure: a knowledge graph of the physical energy system. At Enlit we are presenting its first complete release. The **physical energy ontology** defines, in one consistent model, the objects that matter to an operator (assets and their components, networks and their topology, hazards, environmental conditions, regulation, resources, climate scenarios and decisions) and the relationships between them. Lines, poles, substations, transformers, solar plants, storage, dams, pipelines and electrolysers are described with the same grammar, so that a risk computed for one can be compared, aggregated and acted on alongside any other. This is what lets every capability in Predicterra share the same objects: forecasting, long-term simulation, field operations and regulatory reporting. It is also what makes the twin auditable, and what allows AI to reason over real infrastructure instead of over disconnected tables. Integrate once, then build every new use case on top. ### 3\. Parametric fragility models for energy assets A hazard forecast says how strong the wind will be. It does not say what that wind will do to a specific 30-year-old wooden pole on a ridge, or to a substation built on a floodplain. Predicterra's **parametric fragility models** close that gap. For each asset class, they describe the probability of failure as a function of hazard intensity, with parameters learned from real incident histories and adjusted for asset age, design, condition and exposure. They are physically grounded, interpretable and auditable: an engineer can read them, a regulator can check them, and they transfer to regions where incident data is scarce. Combined with the incident foundation model and the ontology, they turn a weather forecast into what an operator actually needs: the expected failures, asset by asset, with their confidence, days in advance and decades ahead. ## Why Enlit, why now Europe's energy system is being asked to integrate more renewables, more electrification and more extreme weather at the same time, under stricter regulation and tighter budgets. Integration, resilience and competitiveness are not separate agendas. They depend on the same capability: knowing what will happen to every asset before it happens, and which assets will fail first. That is what Predicterra was built for, and these three launches take it a step further. Enlit is where the operators who need it will be. ## Meet us in Vienna **Where:** Spain Pavilion, Enlit Europe 2026, Messe Wien, Vienna **Stand:** B.c80 *(Spain Pavilion)* **When:** November 10 to 12, 2026 We will be running live demos of the incident foundation model, the physical energy ontology and the fragility models throughout the event. If you operate grids, generation, hydro, oil and gas, hydrogen or water assets, we want to show you your own network in the model. → **Book a meeting:** [hello@predicterra.com](mailto:hello@predicterra.com) *Predicterra builds the onto-physical digital twin of the energy system. Physics tells you how your assets behave. Ontology tells you how everything connects.* [predicterra.com](https://predicterra.com/?ref=blog.predicterra.com)