Reference : Load Forecasting with Artificial Intelligence on Big Data

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/28619

Title :	Load Forecasting with Artificial Intelligence on Big Data
Language :	English
Author, co-author :	Glauner, Patrick [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	9-Oct-2016
Peer reviewed :	Yes
Audience :	International
Event name :	Sixth IEEE Conference on Innovative Smart Grid Technologies, Europe (ISGT Europe 2016)
Event date :	from 09-10-2016 to 12-10-2016
Event organizer :	IEEE
Event place (city) :	Ljubljana
Event country :	Slovenia
Abstract :	[en] In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging signal processing problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning for load forecasting with TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications. The advantage of Deep Learning is that the results can easily be applied to other problems, such as detection of nontechnical losses. Attendees will be provided with code snippets that they can easily amend in order to perform analyses on their own time series.
Permalink :	http://hdl.handle.net/10993/28619

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