Leveraging multiple heterogeneous measurements to predict wind power has long been a challenging task in the electrical community. In this paper, a deep architecture incorporated with multitask learning and multimodal learning for wind power prediction, termed predictive stacked autoencoder (PSAE), is presented. PSAE is a unified framework integrating multiple stacked autoencoders (SAEs), one feature fusion layer, and one prediction terminal layer, which expands the architecture from two spatial dimensions, including the depth and width, compared to conventional prediction models. Initially, the SAEs at the bottom of PSAE extracted features from multiple kinds of measurements respectively. Following, the feature fusion layer encodes the high-order features extracted by different SAEs into a unified feature that is more informative and representative for wind power prediction. Finally, the prediction terminal layer functions as a regression machine which generates the predicted targets based on the fusion features. Trained in an end-to-end (E2E) manner, PSAE is capable of learning heterogeneous features jointly and achieving the prediction task of sequence-to-sequence (S2S). Experiments for multi-step short-term predictions are conducted on real-world data, and the results demonstrate the superiority of PSAE to prior methods.

中文翻译：

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共同学习异构特征：用于多步短期风电功率预测的深层端到端框架

长期以来，利用多个异构测量来预测风能一直是电气界的一项艰巨任务。在本文中，提出了一种将风能预测与多任务学习和多模态学习相结合的深度架构，称为预测堆叠自动编码器（PSAE）。PSAE是一个统一的框架，集成了多个堆叠式自动编码器（SAE），一个特征融合层和一个预测终端层，与传统的预测模型相比，该结构从两个空间维度（包括深度和宽度）扩展了体系结构。最初，PSAE底部的SAE分别从多种测量中提取特征。以下，特征融合层将不同SAE提取的高阶特征编码为统一的特征，该特征对于风电预测更具参考价值。最后，预测终端层用作回归机，该回归机基于融合特征生成预测目标。PSAE以端到端（E2E）方式进行训练，能够共同学习异类特征并实现序列对序列（S2S）的预测任务。在实际数据上进行了多步短期预测的实验，结果证明了PSAE优于现有方法。PSAE能够共同学习异类特征并完成序列到序列（S2S）的预测任务。在实际数据上进行了多步短期预测的实验，结果证明了PSAE优于现有方法。PSAE能够共同学习异类特征并完成序列到序列（S2S）的预测任务。在实际数据上进行了多步短期预测的实验，结果证明了PSAE优于现有方法。