Multi-aspect renewable energy forecasting

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Corizzo, R (Author) Other; Ceci, M (Author) Other; Fanaee T, H (Author) Other; João Gama (Author) FEP

Journal

Title: Information Sciences

Vol. 546

Pages: 701-722

ISSN: 0020-0255

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 51 Citations

Scopus - 63 Citations

Other information

Authenticus ID: P-00S-Q4D

DOI: 10.1016/j.ins.2020.08.003

Abstract (EN): The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22