It’s still fresh in everyone’s memory: on Monday, April 28, 2025, a massive blackout hit the entire Iberian Peninsula at 12:33 p.m. In just five seconds, 15 gigawatts of electricity generation were lost, severely impacting critical infrastructure like transport, telecommunications, and hospitals. Power was gradually restored, with 99.95% of demand covered by the following day.
The energy sector operates under conditions of high complexity, interdependence, and constant pressure to improve efficiency, reduce risks, and ensure service continuity. Events like outages, demand spikes, or infrastructure failures highlight how crucial it is to make fast, well-informed decisions. This is precisely where Artificial Intelligence applications under the umbrella of Decision AI can support both strategic and operational decision-making.
In this article, we explore three key Decision AI tools applied to the energy sector:
Energy demand forecasting models
Why it matters:
Accurate demand forecasting is the backbone of efficient grid operations. It allows operators to balance supply, schedule maintenance, avoid imbalances, and lower operational costs during critical situations (such as heatwaves, extreme cold, or sudden disruptions).
What does it do?
It helps anticipate how much electricity will be needed in the coming hours, days, or weeks, enabling more precise planning.
How does it work?
These tools analyze large volumes of data, historical consumption, weather patterns, usage habits, holidays, and more. From that, they generate demand estimates for different timeframes.
Instead of relying solely on averages, they learn from past patterns to adjust forecasts in real time and flag anomalies.
Simulation and digital twins of the power system
Why it matters:
These tools allow for the creation of simulated systems (Digital Twins) to test scenarios before applying them in the real world. They help manage complex situations like cascading failures, bottlenecks, or load redistribution.
They are especially useful in investment planning, where they can evaluate the impact or feasibility of new infrastructure or strategies before committing real-world resources.
What does it do?
In the energy context, it virtually replicates the behavior of the electrical grid to simulate scenarios, validate decisions, and anticipate outcomes under complex or disruptive conditions.
How does it work?
A Digital Twin is a virtual replica of a real-world system. It uses real-time data and mathematical models to simulate the grid’s behavior under various conditions from technical failures to demand changes or new infrastructure deployments.
Economic impact assessment models for outages
Why it matters:
Beyond physical damage, critical failures in the power grid have serious economic repercussions across all sectors.
Being able to reliably quantify these effects is key to making decisions around insurance, compensation, resilience planning, or investment in critical infrastructure.
What does it do?
It estimates the direct and indirect economic losses caused by failures, interruptions, or critical events in the energy infrastructure.
How does it work?
These models cross operational data with economic information: sectors affected, outage duration, type of industry or activity disrupted. They then estimate both direct and indirect costs for companies, cities, or regions, measuring not just kilowatt-hours lost, but also productivity, revenue loss, and ripple effects.
Want to explore use cases tailored to your sector?
The energy sector is at a turning point: decarbonization, decentralization, and digitalization are redefining both its challenges and opportunities. In this evolving landscape, decision-making can no longer rely solely on experience or gut instinct, it needs the support of tools that are robust, accurate, and adaptable.
Technologies grouped under the Decision AI approach provide that support. They allow for more accurate demand forecasting, system behavior simulation under various conditions, and rigorous measurement of economic impacts.
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