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.

可再生能源发电厂的不断增加给电网集成，负载平衡和能源交易等带来了新的挑战，这使其成为提供有效预测模型的基础。文献中的最新方法表明，在来自多个植物的数据中利用时空自相关可以导致更好的预测。尽管张量模型和技术适合处理时空数据，但它们在能量域中很少受到关注。在本文中，我们提出了一种基于塔克张量分解的新方法，该方法能够为学习任务提取新的特征空间。为了进行评估，我们在三个可再生能源数据集中调查了具有新特征空间（与原始特征空间相比）的预测聚类树的性能。