Creating an efficient consumption culture
Harnessing natural light, using energy-efficient lamps or turning off appliances when they are not being used are some examples of how to consume responsibly and efficiently. When we talk about responsible energy consumption, we mean saving energy resources by changing consumption habits to save both energy and money.
One of our challenges is to get our products and services adapted to the needs of our domestic customers, while improving their habits and building a culture of efficient and responsible consumption.
To advance in this customisation, we need to know our customers better and to analyse the consumption of their home appliances or devices. The information available at the moment (hourly consumption and the contract's technical characteristics) is not sufficient to achieve the level of knowledge needed to make recommendations and offer new specific products for each customer.
As a consequence, RC4ALL (Responsible Consumption for all) was devised. This project was carried out in consortium with the Technological Research Institute of the Pontifical University of Comillas. Its objective is to develop a system that, based on the specific information of consumption per device of a small number of customers and enriching it with information from external sources thanks to artificial intelligence, is able to generate personalised recommendations that improve consumption efficiency of any customer. This is only possible by using the most advanced artificial intelligence techniques.
Machine learning to get to know our customers
A key factor of the project is the selection of a sample of customers. As such, it is necessary to analyse all the available information on the population of the various customer segments. Once this representative group of customers is selected, specific measuring equipment is installed in their homes to monitor the consumption of their electrical devices.
From all the data collected about customers (initial data, new consumption data obtained from the installed devices and data retrieved from other sources of information), machine learning techniques will be used to extract the relevant knowledge in each of the planned analytical stages.
In the words of Alicia Mateo, Head of Advanced Analytics Market Iberia, "applying machine learning techniques allows us to improve the knowledge of our customers and their preferences to offer products and services adapted to their individual needs".