Publications

Publications by LIAAD

2021

Meta-aprendizado para otimizacao de parametros de redes neurais

Authors
Lucas, T; Ludermir, TB; Prudencio, RBC; Soares, C;

Publication
CoRR

Abstract

2021

Pastprop-RNN: improved predictions of the future by correcting the past

Authors
Baptista, A; Baghoussi, Y; Soares, C; Moreira, JM; Arantes, M;

Publication
CoRR

Abstract

2021

Model Compression for Dynamic Forecast Combination

Authors
Cerqueira, V; Torgo, L; Soares, C; Bifet, A;

Publication
CoRR

Abstract

2021

Preface

Authors
Soares C.; Torgo L.;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2021

Discovery Science

Authors
Soares, C; Torgo, L;

Publication
Lecture Notes in Computer Science

Abstract

2021

Multi-aspect renewable energy forecasting

Authors
Corizzo, R; Ceci, M; Fanaee T, H; Gama, J;

Publication
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.

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