On the 12th of July, Ibon Salbidegoitia, R&D Director of BigDa Solutions, participated in an informative conference organised by Enpresa Digitala, an initiative of the SPRI in collaboration with BIC Araba.

The conference aimed to analyse the role of Artificial Intelligence (AI) in energy savings. More specifically, how AI, combined with other technologies such as IoT, big data, and advanced analytics, can generate predictive models to help companies know their future energy consumption.

For companies, these predictive models serve as a roadmap of their energy behaviour by allowing them to know in detail and advance how much energy they will need to activate their productive force. Thus, AI offers a tool that enables industries to plan how much they will consume and when and where before these consumptions actually happen.

Ibon also highlighted the main points to consider when deploying an energy forecasting project for the industrial sector:

1. To develop a predictive model based on machine learning with time series to grant accuracy: BigDa Solutions works with predictive models that allow through a digital twin (our BEMP platform) to simulate and compare multiple variables in real-time from historical data.
2. Digitisation is the first step in energy forecasting modelling: Predictive energy consumption models must be fed with data. Wireless communication technologies such as IoT are critical allies for data acquisition.
3. Non-code solutions to create customised variables and parameters: The predictive models that we develop at BigDa Solutions allow companies to create their own variables through non-code solutions that help anticipate their energy consumption exactly when and how they need it, with multiple visualisation options.
4. More than forecasting: AI also allows us to analyse operations thanks to historical consumption data to detect anomalies or overconsumption. This other application of AI allows BigDa Solutions to help companies improve their energy efficiency by spotting possible consumption savings.
5. Simulation to go beyond: Our BEMP platform also allows simulations on how changes in company operations can alter consumption. This information becomes an excellent tool for forecasting the energy impact of possible changes, thus reducing the company's consumption.
6. Simulation to go the extra mile: Our BEMP platform also allows simulations on how company operations changes can alter consumption. This information becomes an excellent tool for forecasting the energy impact of possible changes and thus gives the ability to reduce a company's consumption.

In short, anticipating energy consumption is a tremendous competitive advantage that allows companies to adapt and organise their production to focus on improving their energy efficiency. Thus, AI adds to its countless uses, a solution for the energy sector that will accelerate the energy transition of the Basque and Spanish industries to an increasingly decarbonised and efficient model.

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