A featured contribution from Leadership Perspectives: a curated forum reserved for leaders nominated by our subscribers and vetted by the Construction Tech Review Advisory Board.

Enel

Facilitating Human- Computer Collaboration at the Speed of Thought

Enel is a multi-national power company and a leading integrated player in the world’s renewable energy markets, with a particular focus on Latin America and Europe. We operate in 34 countries across five continents, generating power from over 86 GW of net installed capacity and distributing electricity and gas through a network covering around 2.2 million km. Especially in Europe, with 70 million end-users, we have the largest customer base among our European peers. And, being a data-driven company with the highest number of end-users, we are producing an impressive amount of data, which continues to grow over time. However, it is not only about producing data but also deriving meaningful insights that help optimize our services right from operations to a strategic point of view.

Using Cognitive Prediction in the Energy Sector

At Enel, we wanted our employees to manage the health of equipment by simply looking at a dashboard, instead of manually inspecting the pieces of equipment round the clock. The core idea was to use historical parameters and traditional patterns of events to identify anomalous equipment behavior and deploy our maintenance teams if necessary. Also, as part of our data-driven strategy, we wanted to help our managers analyze different possible scenarios and use predictive algorithms for aspects like financial forecasting. Therefore, we launched a specific program along with our digitalization plan to build a cognitive center made up of 30 data scientists together with software and data engineers. The project aimed to develop cognitive technologies that allow individual decision-makers to work with computers more efficiently than ever before.

This is made possible by enhancing the industry’s ability to source, analyse and filter big data in order to make it more consumable so that decision-makers can act upon the data more effectively.

Machine learning helps us treat each of our customers as a unique individual rather than a cluster of consumers


Leveraging Cognitive Technology

The cognitive center helped us develop machine-learning algorithms in addition to deep learning and computer vision capabilities that help tackle and address specific challenges in our line of business. For instance, now we can detect anomalous equipment performance with the help of machine learning algorithms that are sending alerts in order to take a predictive approach. Moreover, we are doing this across all our renewable and traditional power stations. We are also using a similar approach to develop tailored marketing initiatives that are directed towards customer demands. Machine learning helps us treat each of our customers as unique individuals rather than a cluster of typical consumer behavior patterns and enables us to increase customer retention. We also use the same machine learning capability to analyze the behavior of our employees and understand why they might want to leave our organization. Finally, in an environment of ever-tighter margins, cognitive technology can help energy companies increase the productivity of their oil and gas fields and minimise exploration risk when searching for new resources.

Advice for the CIOs

Cognitive environments have the potential to facilitate human-computer collaboration at ‘the speed of thought,’ which can lead to a more informed and robust decision-making process. Especially, in the energy sector, when it comes to the optimisation of reservoir production, a cognitive environment can adapt to the individual needs of a varied set of technical experts, equipping them with the tools needed to analyse data from numerous sources. Moreover, technicians are also able to tie in existing production models with the analysed data and adjust them so that they accurately match current production as time goes on. So, the aspiring CIOs must look deeper into the promises of cognitive technology. However, this is not a one-off project. They need to constantly evaluate the outcome of their projects against their desired results, and keep improving their capabilities.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.

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