Stream-based electricity load forecast

• Tipo: Capítulo ou Parte de Livro
• Data: 2007

Autores

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

Livro

Título: Knowledge Discovery in Databases: PKDD 2007

Páginas: 446-453

ISBN: 978-3-540-74975-2

ISBN Eletrónico: 978-3-540-74976-9

Editora: Springer

Indexação

ISI Web of Knowledge - 18 Citações

Scopus - 25 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8