Stream-based electricity load forecast

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Joao Gama (Author) FEP; Pedro Pereira Rodrigues (Author) REIT

Book

Title: Knowledge Discovery in Databases: PKDD 2007

Pages: 446-453

ISBN: 978-3-540-74975-2

Electronic ISBN: 978-3-540-74976-9

Publisher: Springer

Indexing

ISI Web of Knowledge - 18 Citations

Scopus - 25 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-DTF

DOI: 10.1007/978-3-540-74976-9_45

Abstract (EN): Sensors distributed all around electrical-power distribution networks produce strean is of data it high-speed. From a data mining perspective, this sensor network problem is characterized by a large number of variables (sensors), producing a continuous flow of data, in a dynamic non-stationary environment. Companies make decisions to buy or sell energy based on load profiles and forecast. We propose an architecture based on an online clustering algorithm where each cluster (group of sensors with high correlation) contains a neural-network based predictive model. The goal is to maintain in real-time a clustering model and a predictive model able to incorporate new information at the speed data arrives. detecting changes and adapting the decision models to the most recent information. We present results illustrating the advantages of the proposed architecture, on several temporal horizons, and its competitiveness with another predictive strategy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8