This course is organized within the framework of the EES-UETP Consortium.

More information can be found at:

http://www.ees-uetp.com/courses/course2018-04.php

Registrations are limited!

A brief description of the course is provided next:

The technological revolution in the electric power system sector is producing large volumes of data with relevant impact in the business and functional processes of system operators, energy utilities and grid users. This course aims to cover different theoretical and practical aspects of data analytics in energy systems, according to the following viewpoint:

(1) The future generation of big data functions will combine spatial-temporal information and distributed learning techniques that exploit recent advances in high performance and distributed computing.

(2) The output should be probabilistic (uncertainty) information and with high value for integration in decision-aid methods under risk.

(3) Deep learning techniques represent an added value for automatic feature extraction and reduction, but manual feature engineering using domain (expert) knowledge cannot be abandoned.

(4) Machine learning algorithms can be used to control grid assets, for instance embedded in reinforcement learning techniques or to create proxy models for complex physical systems.

(5) Creation of new business models for knowledge extraction from data is also expected in a near future. Some examples are data analytics for consumer engagement in demand response, big data pre-processing from grid sensors, electricity markets modelling and predictive maintenance of electrical assets.