Arbitrated Ensemble for Solar Radiation Forecasting

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Cerqueira, V (Author) Other; Torgo, L (Author) FCUP; Carlos Soares (Author) FEUP

Conference proceedings International

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

Pages: 720-732

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

Cadiz, SPAIN, JUN 14-16, 2017

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 7 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13