The impact of big data on energy: a technology that is transforming the industry

6 February, 2023

The energy sector is constantly searching for greater efficiency, lower environmental impact and adaptation to growing demand, which in turn is strained by resource scarcity. Information technologies, including big data, can help with all these challenges.

Big data is the analysis of large volumes of data that can come from different sources, such as internal company information but also from external sources. This technology allows data to be analysed and decisions to be made in real time, always focusing on generating value. In fact, the characteristics of big data can be summarised in the 4 Vs: volume, variety, velocity and value generated.

By having an extensive capacity to analyse large amounts of data, one of its main advantages is the extraction of valuable information to uncover associations and obtain clues that might go unnoticed with a traditional analytical method.

The use of big data facilitates informed decision making and makes it possible to improve the efficiency of operations

Moreover, by processing and analysing vast amounts of data in real time (something that would otherwise be very complex), an organisation can respond quickly to any changes in the environment, adapting to new conditions almost on the spot; and better understand what is happening.

Thus, the use of big data facilitates informed decision-making and makes it possible to improve the efficiency of operations, both of which are very useful in the energy sector. It also increases security by detecting and preventing incidents. Finally, it helps to make the company more competitive and better prepared to face the challenges of the energy transition.

In short, big data makes it possible for companies to evolve from a reactive to a predictive behaviour.

Data analysis in the energy sector

Big data offers four analytical techniques: descriptive, to understand patterns and trends in historical data; diagnostic, to identify underlying causes from patterns; predictive, to make inferences and predict future events; and finally, prescriptive, to suggest actions to improve future outcomes. All of them are useful in the energy sector for, among other things:

  • Adopting technological advances more rapidly for energy production, storage and distribution.
  • Digitising processes to gain in-depth knowledge of consumption habits, users and the types of service required.
  • Improving knowledge of the energy system, adapting to new regulations and anticipating their obligatory nature.
  • Placing the customer at the centre, offering solutions to new demands such as the expansion of renewable energies, the introduction of alternative mobility or the use of smart meters, among others.

Real examples

The energy sector has a great ally in big data to solve two of the challenges it faces on a daily basis:

  • Accurate forecasting: a quality that, in particular, can be very useful in matching energy supply and demand at all times.
  • Anomaly detection: big data helps to predict equipment failure, thereby reducing infrastructure maintenance costs.

There are already several examples of the adoption of big data technologies demonstrating their potential today. Enagás, in collaboration with Keepler and Amazon Web Services, has a solution for the operation and maintenance of its equipment, specifically for cryogenic pumps in liquefied natural gas tanks. Thanks to this solution, much more predictive maintenance is achieved, avoiding possible interferences in the service and achieving considerable savings in resources.

In addition, the combination of big data with other technologies such as blockchain, the Internet of Things or Artificial Intelligence also offer great results for companies.

Big data and artificial intelligence are used together and thanks to these two techniques very accurate results are achieved

Specifically, big data and artificial intelligence are used together and thanks to these two techniques very accurate and robust results are achieved. Artificial Intelligence uses algorithms and techniques to analyse large amounts of data and extract, through machine learning, data mining, machine vision and predictive analytics, valuable information for businesses. In this way, very powerful models are obtained, with more knowledge than ever before, handling reliable data and making it possible to focus decision-making on the basis of this information and thus define some future scenarios.

As the use of big data continues to grow in the energy sector, we will see new products and services being developed that further improve the efficiency and sustainability of the sector.