Arbitrated Ensemble for Solar Radiation Forecasting

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Cerqueira, V (Autor) Outra; Torgo, L (Autor) FCUP; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I

Páginas: 720-732

14th International Work-Conference on Artificial Neural Networks (IWANN)

Cadiz, SPAIN, JUN 14-16, 2017

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00M-V8R

DOI: 10.1007/978-3-319-59153-7_62

Abstract (EN): Utility companies rely on solar radiation forecasting models to control the supply and demand of energy as well as the operability of the grid. They use these predictive models to schedule power plan operations, negotiate prices in the electricity market and improve the performance of solar technologies in general. This paper proposes a novel method for global horizontal irradiance forecasting. The method is based on an ensemble approach, in which individual competing models are arbitrated by a metalearning layer. The goal of arbitrating individual forecasters is to dynamically combine them according to their aptitude in the input data. We validate our proposed model for solar radiation forecasting using data collected by a real-world provider. The results from empirical experiments show that the proposed method is competitive with other methods, including current state-of-the-art methods used for time series forecasting tasks.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13