Keeping metro commuters comfortable in the summer heat is no easy task. It can take huge amounts of energy to power the ventilation systems that keep the air flowing—and the temperatures down. Metro de Madrid knew this better than most.

As the seventh longest metro system in the world, on average, 2.3 million commuters use Metro de Madrid’s network of 294 kilometers of track and 301 stations every day. To help passengers stay cool inside stations, particularly during the hot summer months, Metro de Madrid operates 891 ventilation fans, which were consuming as much as 80 gigawatt hours of energy annually.

Conscious of the need to save energy and reduce costs, Metro de Madrid were looking for fresh thinking. The goal: keep metro temperatures comfortable in the most efficient way possible.

Strategy and Solution

Sustainable ventilation for an entire metro system

The Madrid Metro Ventilation experts worked with Accenture Applied Intelligence to develop a system that took inspiration from an unusual source: the coordinated foraging behavior of a bee colony.

The system deploys an optimization algorithm that leverages vast amounts of data to explore every possible combination of air temperature, station architecture, train frequency, passenger load and electricity price throughout the day.

The algorithm uses both historic and simulated data, factoring in outside and below-ground temperatures over the next 72 hours. Because the algorithm uses machine learning, the system gets better at predicting the optimal balance for each station on the network over time.

Tracking for failure

The system also includes a simulation engine and maintenance module, which allows for, among other things, tracking for failures in the fans’ operation. This enables Metro de Madrid to easily monitor and manage energy consumption, identify and respond to system deficiencies, and pro­actively conduct equipment maintenance.

The system took inspiration from an unusual source: the coordinated foraging behavior of a bee colony.


The artificial intelligence (AI)-based system has enabled Metro de Madrid to reduce its energy costs for ventilation by 25 percent and cut CO2 emissions by 1,800 tons annually.

Reduced energy costs

Metro de Madrid are set to see energy costs reduce by an extraordinary 25% annually.

CO2 emission reduction

CO2 emissions across the whole metro network are expected to be cut by up to 1,800 tons.

Increased sustainability

The self-learning ventilation system has provided a major boost for city sustainability.

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