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Artificial intelligence to improve our services

Artificial intelligence and the new voice assistant technology allow Endesa to develop new skills to meet customers' needs and advise them on more efficient energy consumption.

Talking of "Artificial Intelligence" is speaking of a broad range of solutions (machine learning, deep learning, cognitive tools, etc.) that are being incorporated into our lives at breakneck speed and which are revolutionising industries and sectors, including the energy sector. Technology will be a determining factor in the change of energy model that we have been experiencing in the last century.

But are the advantages of these technologies? For which tasks in our daily routine will they be really useful?

AI in commercialization

Customer service is one of the areas in which this kind of technology is being applied and where virtual voice assistants are causing a revolution. According to data from Comscore, almost 25% of mobile searches are performed by voice and it is expected that this will rise to 50% in the year 2022.

Thanks to artificial intelligence, these assistants can not only give us specific information, but they also learn from our experiences and improve the outcomes of their communications especially when requesting help.

“Who is not familiar now with Siri (Apple) or Alexa (Amazon) or has not seen Google Assistant on their mobile phone?”

In the case of Alexa, things go a step further because Amazon has launched Echo in Spain, the intelligent speaker that connects to its virtual assistant so that it can interact with her from any location in the household.

Today it is possible to ask Alexa using your own voice to play music, read a book or tell you tomorrow's weather. But furthermore, thanks to the new Endesa skill, it is also possible to ask about your energy and have tailored energy advice that enables saving on the bill closer than ever. And the only thing needed is being an Endesa client and having one of the different Amazon Echo wireless speaker models.

Thanks to this, at Endesa we offer a customer service channel by means of a device that enables the customer to control all the home automation. By combining both factors, we coordinate our customer service with information on consumption and bills together with the possibility of controlling the home’s temperature, lighting, etc. Therefore, this is an all-inclusive service that empowers the customer and gives them more decision capacity on saving and makes energy expenditure more efficient.

Our intention is to continue to make progress on this development so that by using their voice customers can carry out the same dealings or queries as they have been able to do up to now on the company's website or app. In future Alexa can even be used to open up communication with the contact centre in case additional service is required.

Moreover, we will continue to develop new voice channels to be able to reach all our customers. Along these lines, we are preparing a “skill” for Google Home which we hope will be up and running soon.

At Endesa we are also using Watson, IBM’s artificial intelligence to help customers both in the chat and by telephone and we do so with a significant percentage of clients attended to autonomously; all of this is to develop new solutions and optimise costs more quickly. After a pilot experience in 2017, last year it was extended to the entire customer service centre and currently helps customers both on the chat and telephone with a significant percentage of people attended to autonomously.

Intimately related to AI and the customer, at Endesa we are also working along these lines:

  • Models to prevent non-payments: creation of machine learning analytical solutions to reduce the number of non-payments and increase the charging rates in the non-payments portfolio.
  • RPAs. Data coder robots.
  • Complaints classifier. CRM complaint classification based on text recognition.
  • MEGABAT: Forecast of time demand for domestic customers that enables having stable consumption series over time, the most independent of commercial activity, customer ins and outs, thereby achieving savings and more speed in the service.
“The forecast of time demand, the reduction of the number of non-payments, the CRM complaint classification and the data coder robots are some of the objectives of the commercialization.”

IA in distribution networks

We are also working to make progress on digitalisation of the distribution network by means of incorporation of artificial intelligence techniquesbig data in energy, and thereby improve our services.

Therefore, PASTORA was set up; the project to improve real-time control and preventive maintenance of the distribution network that carries electricity to homes and that will use big data, deep learning and artificial intelligence technologies to use the millions of data offered by the intelligent network. The aim is to develop predictive models of the network’s behaviour and therefore improve its operation.

The big data has reached the electrical network and will change everything. The millions of data offered by intelligent networks will enable system operators to predict and anticipate possible incidences to improve the network’s operation and increase the quality of customer service.

Within this digitalisation process, at Endesa we have also implemented the LARS system (Breakdowns and Re-establishment of Supply system), an automatic system that operates when a breakdown occurs as a virtual operator and performs from the same control centre system - just as human operators would do - operations on the network necessary to isolate breakdowns and re-establish supply in less than three minutes. LARS is responsible for remote management and will do so much more in the future.

Work is in progress to integrate historic data from breakdowns, failure rates, construction maps of streets and weather forecasts among other information so that the system can determine more accurately at what point in the line the breakdown occurred. Using big data and Artificial Intelligence techniques to process all this information, for example where lightning struck, where construction work on the street that may affect a cable is being carried out, the likelihood of determining the origin of the incidence more quickly increases significantly.

“La inteligencia artificial también ha cambiado la distribución de la energía: desde el aumento de velocidad en la localización de averías hasta la creación de algoritmos para reducir el fraude eléctrico.”

We also use artificial intelligence to respond to fraud. We are developing an algorithm that enables detecting fraud more reliably. This algorithm, responsible for Machine Learning, has been applied since 2017 all over Spain. The application of Machine Learning and Deep Learning algorithms on Big Data is enabling many of the company's operations to be approached more efficiently including the response to fraud.


IA in generation

And while AI helps us to see to clients better and move forward in the digitalisation of our distribution network, we are also incorporating it into our generation business project line.

  •  Predictive Diagnosis: Machine learning and operational data analysis techniques are used to detect and diagnose faults in principal plant equipment with the purpose of reducing lack of availability and avoiding breakdowns.
  • Supervision cameras with artificial vision: Development of intelligent visual models by means of using neuronal networks for automatic identification of operational events and problems.
  •   MOP: Pilot project to introduce a system to help the operation of generation plants based on artificial intelligence and cognitive models.
  • Monitoring of motors and low and medium tension switches: Use of advanced sensors and artificial intelligence algorithms to detect electrical component failures early.
  • Digital Report: Development and implementation of a single global system for generation of reports with access to data from the most important generation applications and incorporating a digital information voice assistant.
  •  AI4OM – Artificial Intelligence For O&M: Proof of concept to develop a prescriptive maintenance tool able to predict the degree of reliability of events detected by predictive tools and automatically suggest an appropriate maintenance action for the situation.
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