We apply AI and advanced analytics in order to generate efficiencies in our processes and thus increase the availability of our infrastructures, boost the integration of renewables and improve the safety of our professionals.
Electric grid infrastructures and asset management
Completed
Red Eléctrica is implementing a new vegetation management model for its power lines. Until now, the identification of vegetation species was done through manual photo-interpretation techniques.
Through this project, alongside our partner Overstory, we have created a series of algorithms that automate the photointerpretation of tree and shrub species from satellite images and images from the National Aerial Orthophotography Plan (PNOA).
Species identification for the province of Zamora.
The analysis is extended to the rest of the geography, addressing the identification of species according to each region.
To optimise activities and transversal processes
Active
Enables the budgeting and planning of investment projects in the Transport Network using all the information available and specific to each project. These are feedback and automatic learning models to be applied during the life cycle of the projects.
Objective: to improve management of the investment activity of the company and decision-making in the DC and DGT based on the use of the information generated in the activity of the projects.
To deploy a new version of the cost and duration estimation models for investment projects.
Increase employees safety and wellness
Completed
The goal of this project is to switch from a reactive/preventive model to a predictive model in the area of occupational health and safety. We make use of the traceability and digital footprint of the data and information provided by the management tools and applications of the Red Eléctrica Group to make analyses with descriptive (what happened), predictive (what could happen) and prescriptive models (what can we do to stop it happening) The first goal of the project is to use Artificial Intelligence methods to create a probability indicator of the risk of accidents and issues associated with any maintenance and construction work.
Concept and data modelling.
Data collection, ordering and processing.
Data analysis and visualisation of the results.
A foundation is created for applying models for predicting the risk of accidents and issues related to maintenance and construction work.
Operation of the electrical system and integration of renewables
Active
The CONPP project aims to develop a methodology for forecast probability intervals calculation, combining the intervals of the individual predictors of the suppliers with the best accuracy, which provide the hourly demand and production forecast curves at the level of each system. The main objective of this work is the development of a procedure for the construction of prediction intervals associated to forecasts obtained as combinations of independent model and supplier predictors.
• Definition of methodology to calculate the probability bands of the combined wind forecast from the probability bands of the individual forecasts participating in the combination.
• Definition of methodology to assess the goodness of fit of the probability bands provided by the individual forecasts of the different suppliers.
• Exploration and evaluation of its implementation in the field of renewable production (wind and photovoltaic) and demand, as well as to apply it to all peninsular and non-peninsular systems.
