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

• Type: Article in International Scientific Journal
• Date: 2007

Authors

Catalao, JPS (Author) Other; Mariano, SJPS (Author) Other; Mendes, VMF (Author) Other; Ferreira, LAFM (Author) Other

Journal

Title: Engineering Intelligent Systems

Vol. 15

Pages: 15-23

ISSN: 1472-8915

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 14 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9