2021
Autores
Lucas, T; Ludermir, TB; Prudencio, RBC; Soares, C;
Publicação
CoRR
Abstract
2021
Autores
Baptista, A; Baghoussi, Y; Soares, C; Moreira, JM; Arantes, M;
Publicação
CoRR
Abstract
2021
Autores
Cerqueira, V; Torgo, L; Soares, C; Bifet, A;
Publicação
CoRR
Abstract
2021
Autores
Soares, C; Torgo, L;
Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2021
Autores
Corizzo, R; Ceci, M; Fanaee T, H; Gama, J;
Publicação
INFORMATION SCIENCES
Abstract
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.
2021
Autores
Veloso, B; Gama, J; Malheiro, B;
Publicação
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management
Abstract
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