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Big Data: when data improve the energy efficiency of networks


By correlating all the data obtained, we can achieve an optimum energy configuration for each situation and each type of site.


Optimisation of technical sites, real-time alerts, sharing best practices: with increasingly accurate measurements of its energy consumption, Orange is building more efficient and less energy-intensive networks.

Between 2012 and 2017, when upgrading its mobile networks, Orange launched a plan for deploying probes that measure energy across a sample of its technical sites. The main aim of this upgrading was to improve the quality of service for customers in every country in which Orange has a presence. However, the new equipment is also more energy-efficient and has enabled Orange to control the energy consumption of its mobile networks. “The suppliers had announced energy-saving figures that we were able to check and refine from field data, country by country, thanks to our metering project,” indicated Carole Paganus, in charge of ITN Energy Consumption forecasts for Orange. Today, several thousand sites are equipped with these sensors, a means of better understanding how technical sites actually operate.

Identifying the source of consumption

To optimise energy consumption, you first need to measure it! (“If you cannot measure it, you cannot improve it,” according to Lord Kelvin). Measuring it makes it possible to find out, for example, when the peaks of consumption take place, to understand the impact of traffic (in field configuration), temperatures, public holidays, etc.
Measuring energy consumption in the information systems and networks allows us to follow the energy consumption of our networks in real time and anticipate its changes by data analysis and modelling,” explains Carole Paganus.

Linking information

By increasing awareness of the consumption profiles of its sites and by correlating the consumption data with other information, Orange is able to identify cases of abnormal energy consumption.

The data gathered in recent years will allow other improvements to be made: “We will be able to achieve an optimum configuration for each situation and each type of site. We have also launched strong lobbying efforts among our suppliers to benefit directly from meters embedded in the equipment, so that each of them transmits their consumption natively.” A better understanding of sites also means optimising the energy bill, to adjust contracts – subscriptions, power, etc. – to be as close as possible to actual consumption.

Detecting malfunctions

Big Data has another clear advantage: it provides real-time data. This regular monitoring means we can detect malfunctions across the network. In some countries, many technical sites are air-conditioned to cope with the heat and an air conditioner may continue to operate (and, therefore, consume energy) but without fulfilling its cooling role or, on the contrary, cooling more than the set point, while using more energy. Without measuring consumption and temperatures, these malfunctions cannot be detected and the optimum temperature ranges are not respected. With the measurements taken from the probes, alerts are faster and more precise: this has led to a marked improvement in energy efficiency.

Communicating good practice

The data alone are not the only factor for optimising energy consumption. Orange has also launched a “Big Data Energy” community to encourage those in the countries in which Orange has a presence to share their experiences. “More than 20 use cases have been identified, some of which have already been deployed – and that’s just the start. The many research tasks carried out by Orange in the field of Big Data and Artificial Intelligence will also enhance this ecosystem, making Big Data Energy decision support tool, but also a meaningful community for discussions driven by a common ambition for energy efficiency.”


By correlating all the data obtained, we can achieve an optimum energy configuration for each situation and each type of site.


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