An artificial neural network approach for short-term electricity prices forecasting

• Tipo: Artigo em Revista Científica Internacional
• Data: 2007

Autores

Catalao, JPS (Autor) Outra; Mariano, SJPS (Autor) Outra; Mendes, VMF (Autor) Outra; Ferreira, LAFM (Autor) Outra

Revista

Título: Engineering Intelligent Systems

Vol. 15

Páginas: 15-23

ISSN: 1472-8915

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 14 Citações

Outras Informações

ID Authenticus: P-004-BC7

Abstract (EN): This paper presents an artificial neural network approach for short-term electricity prices forecasting. In the new deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. A three-layered feedforward artificial neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting the next 168 hour electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed approach, reporting the numerical results from a real-world case study based on an electricity market.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9