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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2007

Autores

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

Ata de Conferência Internacional

Título: 2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS APPLICATIONS TO POWER SYSTEMS, VOLS 1 AND 2

Páginas: 411-416

14th International Conference on Intelligent System Applications to Power Systems (ISAP 2007)

Kaohsiung, TAIWAN, NOV 04-08, 2007

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 20 Citações

Outras Informações

ID Authenticus: P-004-F71

DOI: 10.1109/isap.2007.4441655

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: 6