Reference : Introduction to Machine Learning for Power Engineers
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Computational Sciences
Introduction to Machine Learning for Power Engineers
Glauner, Patrick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. We deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car. In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has led to adaptable, more generic methods, that require less expert knowledge and that are easier to deploy in a number of use cases. This is an introductory level course to discuss what machine learning is and how to apply it to data-driven smart grid applications. Practical case studies on real data sets, such as load forecasting, detection of irregular power usage and visualization of customer data, will be included. Therefore, attendees will not only understand, but rather experience, how to apply machine learning methods to smart grid data.
10th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC 2018)
from 07-10-2018 to 10-10-2018
Kota Kinabalu

File(s) associated to this reference

Fulltext file(s):

Open access
Introduction to Machine Learning for Power Engineers.pdfPublisher postprint4.83 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.