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Home » People » Past collaborators » Laiz Souto

PhD. Thesis: Data-driven approaches for event detection, fault location, resilience assessment, and enhancements in power systems

Laiz Souto

Contact Information

Universitat de Girona
Campus Montilivi
Escola Politècnica Superior (Edifici P4)
Carrer Universitat de Girona, 6
17003 – Girona (Spain)
exit@udg.edu

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eXiT Research group - Control Engineering and Intelligent Systems (IIiA - UdG)

The Energy Forecaster tool provides hourly forecasting of electrical/loads and Photovoltaic/wind generation. Two forecasting horizons are provided: intra-day and day-ahead. Forecasts are based on weather data, characteristics of generation assets, and contextual information. Occupancy also can be considered as an input for forecasting. The application is fully integrable in the Energy Management System.

More information: https://elandh2020.eu/ and https://exit.udg.edu/

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No 824388

The Optimal Scheduler tool provides an hourly scheduling of storage (when
store or consume) and controllable assets (when switch on/off) in order to maximize the use of renewable energy resources. It is based on the forecast production/consumption in the Local Energy System (LES). The application is fully integrable in the Energy Management System. This tool is part of the E-LAND project.

More information: https://elandh2020.eu/ and https://exit.udg.edu/

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No 824388

A brief introduction to fault detection, location, and diagnosis with Principal Component Analysis (PCA).

Laiz Souto

The eXiT group is an interdisciplinary research group of the Institute of Informatics and Applications of the University of Girona involved in national and international research and transfer projects.

The main research activity of the group is focused on the application of artificial intelligence principles (data mining and knowledge discovery, qualitative reasoning, case based reasoning, auctions, etc.) and machine learning to support decision-making processes. This research is being conducted in mainly two application domains: medicine/Healthcare and Smart cities/smart grids.

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