On April 28-th, 2025, a massive blackout took place in Spain, Portugal and parts of South Western France. The causes are still being investigated. Apparently it was not a cyberattack. Hypotheses of a strange atmospheric event ( a “fluctuation”) as the cause, or a particularly intense Solar storm, like the Carrington event, are on the table. The chart below shows how sudden and severe the drop in energy consumption has been (source REE / El Pais):
Ontonix has performed a complexity analysis of the Real, Expected and Programmed energy consumption data in the period 22-nd to 29-th April, 2025 (data source). Two types of analysis have been performed:
- A dynamic analysis of the data spanning the entire period in question (22-nd till 29-th April)
- A day-to-day comparison of data similarity
The dynamic analysis produced two measures of interest: complexity and resilience. The chart shown below illustrates how complexity started to peak already on the 26-th. This is the first early warning, two days before the blackout. The red dashed line indicates maximum complexity levels during normal operating conditions.
A second peak – the second early warning – occured on April 27-th. As for resilience, its evolution is depicted in the chart below:
During normal operation, the resilience of the system is, as expected, very high, close to 100%. A slight drop of 2% takes place on April 26-th and a major drop of 5% is recorded on April 27-th (see black arrow).
The daily data comparison provides a measure of similarity of the Real, Expected and Programmed energy consumption data on a particular day with respect to the previous day. When similarity is very high, i.e. close to 100%, one may deduce that the situation is stable.
Until April 25-th the situation was stable, with similarity oscillating between 95% and 98%. On April 26-th this drops to 77%, then to 72% on the 27-th. This means that an early warning could have already been issued on April 26-th, confirming the results of the complexity and resilience analyses.
This simple analysis illustrates how QCM is able to capture the dynamics of complex systems, such as the electricity distribution network in a large and industrialized country, even with data of very low granularity. It also shows how super complex systems are inherently fragile and can suddenly do unexpected things. Highly complex systems, when in proximity of their critical complexity, can collapse catastrophically without the need of external disturbances or shocks.
PS. Could this have been done with AI? Nope! There are not enough such events to learn from. The only alternative is to monitor complexity in real time and watch out for peaks.
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Artificial Intuition 
There might be so many factors or precursors to risk but complexity factorizes them all. Complexity is the proxy that of measured quantitatively with the power of Ontonix, it would serve as the early warning signals of risk that help decision makers act wisely in the unknowns. Instead of pursuing distributions, rather pursue dynamics in today’s world filled with increasing interconnectedness, shocks & discontinuities.
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