An important telecommunication operator successfully bets on critical assets thanks to  the benchmarking of energy consumption.

““Now, it is clear which are the correct actions I have to pursue in order to fulfill my plan. It is very useful to find out how to obtain the maximum benefit from my assets in the shortest possible time ” – Financial Planner and Investment Officer

When you are trying to make improvements, the first question you or your management might ask is “How are we doing?” and the next is “how do we know?”

Benchmarking helps to answer these important questions. In other words, benchmarking is the process of comparison of your energy performance with something similar.Something similar” means either the same asset, comparing its current performances with those of the same period of last year, or another similar asset.

Why benchmark energy consumption?

Understanding your organization energy consumption is the first starting point when considering any energy efficiency plan. By implementing a proven energy management strategy, such as energy benchmarking, you can achieve positive results on profits fairly quickly. This applies in particular to the energy monitoring in the telecommunication filed, because they dispose of a large number of distributed assets.


In this case study, the customer is an Italian subsidiary of a multinational company of telecommunication services. The firm provides UMTS-based mobile phone services, Broadband Internet and Digital Mobile TV services. As phone network the company provides 3G and 4G services (also 2G through partnership with other network operators).


The described Telecom owns 13000 Radio-Base Stations (RBS) and it monitors only 300 of them. The monitored data involve consumption and temperature data. This information was enough for us, since the main energy users in Radio Base Stations are conditioning systems. Annually the company spends around € 36. 000.000 on energy.

For a very long time the Telecom wanted to perform efficiency actions on their assets. However, due to the large number of sites to operate on, it was impossible to implement a joint action plan for all sites.

For this reason, the Telecom realized that they needed to know their most critical assets first and only then decide which actions to take, where and at what cost.

According to this, we understood that a good starting point for system improvement was to obtain a clear picture of equipment energy usage and highlight the most critical RBS


Through our software tool we have designed a solution that allowed the Telecom to identify low energy performance sites, to invest strategically in energy efficiency operations and monitor their effectiveness. This software identifies several possibilities for an asset energy performance improvement. By comparing the energy efficiency of Telecom’s assets, our tool has enabled to set the operations priorities and discover different ways to achieve savings.

The described solution is a complex analysis where, at the beginning, all energy users have been divided in energy consumption clusters. For each cluster a consumption model is identified, which allows to define the theoretical consumption (baseline) of each users. This model is based on the site characteristics and operational conditions (i.e. Degree Days in a year). Finally, an energy efficiency indicator is defined by comparing the actual and theoretical consumption for each site.

The comparison among the relative efficiencies of the different energy users allows to:

  • – check a critical number of users through a Distribution Matrix;
  • – verify the most critical energy assets through an Assessment Matrix.

For the abovementioned process execution, we needed some historical data. The customer disposed of billing information, environmental and geographical data of each RBS and meter data coming from all 300 monitored sites.


Through benchmarking analysis for energy efficiency it was possible to:

  • Identify 40 inefficient assets from an energy point of view. The customer confirmed that some of those had already been selected for a deeper control in response to malfunctioning identification;
  • Discover the most efficient assets within the clusters. Thanks to this evaluation we were able to define the best practices to follow through the direct comparison between efficient and inefficient assets;
  • • Estimate that with a restorative action on the 40 worst sites to bring their efficiency levels to the average of their respective cluster, it would have led to a 7% reduction of the total energy consumption.

Now the customer is working in order to verify the efficiency of its data acquisition system. Furthermore, it has also started to analyze the identified critical assets.

To know more about this case-study or about other energy efficiency improvement projects, contact us!


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