Wind power forecasting helps with the planning for the power systems by
contributing to having a higher level of certainty in decision-making. Due to
the randomness inherent to meteorological events (e.g., wind speeds), making
highly accurate long-term predictions for wind power can be extremely
difficult. One approach to remedy this challenge is to utilize weather
information from multiple points across a geographical grid to obtain a
holistic view of the wind patterns, along with temporal information from the
previous power outputs of the wind farms. Our proposed CNN-RNN architecture
combines convolutional neural networks (CNNs) and recurrent neural networks
(RNNs) to extract spatial and temporal information from multi-dimensional input
data to make day-ahead predictions. In this regard, our method incorporates an
ultra-wide learning view, combining data from multiple numerical weather
prediction models, wind farms, and geographical locations. Additionally, we
experiment with global forecasting approaches to understand the impact of
training the same model over the datasets obtained from multiple different wind
farms, and we employ a method where spatial information extracted from
convolutional layers is passed to a tree ensemble (e.g., Light Gradient
Boosting Machine (LGBM)) instead of fully connected layers. The results show
that our proposed CNN-RNN architecture outperforms other models such as LGBM,
Extra Tree regressor and linear regression when trained globally, but fails to
replicate such performance when trained individually on each farm. We also
observe that passing the spatial information from CNN to LGBM improves its
performance, providing further evidence of CNN's spatial feature extraction
capabilities.

本文提出了一种融合卷积神经网络和循环神经网络的 CNN-RNN 结构，使用来自地理网格多个点的气象数据和风电场的时间信息进行日前风电预测，并在全球预测中取得了较好的结果，与传统模型相比，更好的实现了空间特征提取。

一种用于多步风力预测的并行 CNN-RNN 方法

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting

Graph convolution network based approaches have been recently used to model
region-wise relationships in region-level prediction problems in urban
computing. Each relationship represents a kind of spatial dependency, like
region-wise distance or functional similarity. To incorporate multiple
relationships into spatial feature extraction, we define the problem as a
multi-modal machine learning problem on multi-graph convolution networks.
Leveraging the advantage of multi-modal machine learning, we propose to develop
modality interaction mechanisms for this problem, in order to reduce
generalization error by reinforcing the learning of multimodal coordinated
representations. In this work, we propose two interaction techniques for
handling features in lower layers and higher layers respectively. In lower
layers, we propose grouped GCN to combine the graph connectivity from different
modalities for more complete spatial feature extraction. In higher layers, we
adapt multi-linear relationship networks to GCN by exploring the dimension
transformation and freezing part of the covariance structure. The adapted
approach, called multi-linear relationship GCN, learns more generalized
features to overcome the train-test divergence induced by time shifting. We
evaluated our model on ridehailing demand forecasting problem using two
real-world datasets. The proposed technique outperforms state-of-the art
baselines in terms of prediction accuracy, training efficiency,
interpretability and model robustness.

本文研究了基于图卷积网络的多模态机器学习模型，通过引入多模态互动机制，去增强多模态协调表示的学习能力，从而在预测准确性、训练效率、可解释性和模型鲁棒性等方面，优于现有的基准模型。