Disrupting Energy Investment

Energy companies have been laggards in the 4th industrial revolution since its very beginning. This has caused most of them to be behind other sectors that have already been digitally transformed and suffer the consequences in the shape of low productivity and decreasing margins.

The lag in the adoption of the new paradigm means that there are huge amounts of data that have been lost, not used, stacked in silos, with internal segmentation, communication problems and the lack of a unified source of truth. All these issues mixed with the absence of visible pressure to evolve has caused almost every company in the sector to be complacent and to stay far behind other similar companies in sectors like telco, banking or productive industry.

Nowadays, all those issues mean that energy and utilities companies may be outrunned, both by digital native startups and digital giants that may enter the highly regulated sectors, as is already happening in the banking industry. That is a risk that few companies are willing to take, but at the same time do nothing to avoid it.

In this environment, Red Eléctrica de España decided three years ago to start an ambitious plan to transform its operations and processes from the inside. At the very core of their investment projects, where the high voltage electricity networks are planned, built, and operated, the Company identified some needs, a few risks, and decided to start exploiting the data available, both from internal and external sources. The first step was to explore and store all relevant historical data, just to realize that there was less data than it could be expected, as a result of the lack of a data driven culture. At that point, they decided, helped by Stratio, to start using relevant sources of external public data, in different shapes, like geospatial layers, weather historical and prediction models, administrative public records and many other sources, to develop tools to predict actual costs of investments, providing immediately with an estimate of costs, improved planning times, and ROI estimators to support the company’s decision making process.

That was only the beginning, due to the fact that sometime after the company started to:

· Search for more internal data, and generate synthetics from expert knowledge.
· Improve and enhance their own geospatial dataset including the new relevant parameters.
· Detect and begin to automate the business-as-usual processes.
· Develop a new predictive and quantitative model to detect the risks for the investments projects.
· Produce automated and integrated scheduling and resource planning.

And more and more user cases that had been adding value to the company up to the current point, where processes and applications are being integrated. To fully obtain the gains of all these efforts there are some developments needed in the near term, like:
· Training risk models using neural networks to increase their accuracy in a low historic dataset environment.
· Provide proactive recommendations and insights from current status of investments.
· Enhance the predictive models with continuous learning.

As everything converges in the development of intelligent agents, projects are becoming more technologically supported, reducing human error, boosting the productivity, supporting the management decision process of the investment projects developed in Red Eléctrica de España.