Monitoring: the first step for energy efficiency

By Martin Vahi

 “If you can’t measure something, you can’t improve it.”


This is one of the most famous quotes of Lord William Thomson Kelvin, the man who contributed to the creation of the second law of thermodynamics and invented the absolute temperature scale. A true icon of thermodynamics.


The new ministry for ecological transition, as part of the Recovery Plan project, will have a budget of almost 70 billion to implement an ecological transaction that will involve many aspects of our daily lives, including improving the efficiency of public and private buildings. Proper monitoring is the foundation of any green improvement process related to the structures we live in every day.

It is a fundamental step towards the improvement required by a correct energy-saving process to be able to record and monitor thermo-hygrometric variables and air quality in the environment.



If we analyse this word and compare it to our daily lives, we quickly understand that to “economise” (in any field) we must eliminate waste and anything superfluous. Therefore, saving energy is equivalent to eliminating waste. Monitoring is only possible if there is an expert who can read, understand and analyse the data collected by a specific platform, intervene on the system being monitored to improve the efficiency of its regulation dynamics, and eliminate waste and over-regulation. Monitoring represents the first step towards the implementation of manual actions, which are – presumably – channelled through software and a remote management system.

The question is, what if we want something more? What if we wanted a system capable of proactively monitoring and acting on the regulation of our systems, making them more efficient and eliminating all waste?

Artificial Intelligence


We should rely on Artificial Intelligence: a system that can analyse the variables collected in the environments we live in every day, understand their dynamics and the intrinsic dynamism linked not only to “static” variables but also to any changes in the load to which the systems are subjected as a result of active human occupation. Based on the data collected and the consequent elaborations made by machine learning algorithms – which are therefore able to learn and evolve – the system must then proactively activate itself with targeted regulation interventions that optimise the energy of the systems while preserving the comfort of the occupants.

In this way, we will have an autonomous system, capable of keeping track of the variables trends (monitoring) and intervening to give us adequate comfort and remove all the waste with consequent reduction of emissions and operating costs.
Enerbrain works with this objective in mind: through the use of adaptive and predictive logic, our algorithm optimises the performance of the systems in real time, guaranteeing results in terms of:

  • energy saving
  • improvement of internal environmental comfort
  • CO2 and carbon footprint reduction

If you can’t measure something, you can’t improve it. But if you can measure it and proactively intervene in the systems, you can improve it much more!

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