Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2020.08.003 | Information Sciences |
Keywords | DocType | Volume |
Time series,Forecasting,Energy,Machine learning,Multi-aspect analysis,Tensor factorization | Journal | 546 |
ISSN | Citations | PageRank |
0020-0255 | 2 | 0.40 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Corizzo | 1 | 24 | 7.49 |
| Michelangelo Ceci | 2 | 737 | 86.28 |
| Hadi Fanaee-T | 3 | 75 | 8.55 |
| João Gama | 4 | 3785 | 271.37 |