Spatiotemporal Attention Networks for Wind Power Forecasting

Fu, Xingbo; Gao, Feng; Wu, Jiang; Wei, Xinyu; Duan, Fangwei

doi:10.1109/icdmw.2019.00032

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…As shown in Table 8, in multivariate prediction, the prediction effects of Transformerbased methods other than Reformer [56], such as LogTrans [52] and Informer [55], outperformed the RNN-based methods, such as LSTMa [5]; the performance of TCN [22] further outperformed Transformer-based methods; compared with these methods, SCINet model achieved better performance, because the downsample-convolve-interact architecture enabled multi-resolution analysis, which facilitated extracting temporal relation features with enhanced predictability. Overall, in this paper, as shown in all subtasks with ETT data, the prediction performance using SFINet was all better than that using SCINet-see the relative performance improvement given by RIP as shown in green color.…”

Section: Prediction Experiments and Results Analysismentioning

confidence: 99%

“…Therefore, various Transformer-based TSF methods were presented in [51], as shown in Figure 1b. The multi-head self-attention mechanism is used to extract the spatial correlation between wind farms [52]. Models based on convolutional neural networks (CNNs), such as temporal convolutional networks (TCNs), are also used in time series forecasting (TSF) [53,54].…”

Section: Deep Learning-based Wind Power Forecastingmentioning

confidence: 99%

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SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle–and–fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model’s sequential modeling capability. Finally, the developed shuffle–and–fusion interaction network model is tested using real-world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software.

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Section: Prediction Experiments and Results Analysismentioning

confidence: 99%

Section: Deep Learning-based Wind Power Forecastingmentioning

confidence: 99%

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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“…Attention was first introduced by Bahdanau et al [22], and has become an integral part of sequence modelling using DL. With regards to wind, attention mechanisms have mainly been used to extract temporal tendencies in time-series data, and thereby improve RNN-based forecasting models [23,24,25]. Due to the success of attention-based sequence models, there has been a surge in the application of such techniques to other DL domains, such as the development of Graph Attention Networks [26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

Bentsen

Warakagoda

Stenbro

et al. 2022

J. Phys.: Conf. Ser.

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With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.

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“…Since then, this mechanism has been applied to other tasks, including speech emotion recognition [24], music generation [25], and human action recognition [26]. Interestingly, this mechanism can take both temporal and spatial dependencies [23,27] of a sequence of inputs into account.…”

Section: Introductionmentioning

confidence: 99%

AttendAffectNet–Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention

Thao

Balamurali

Roig

et al. 2021

Sensors

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In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, these typically, while ignoring the correlation between multiple modality inputs, ignore the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture—namely AttendAffectNet (AAN)—uses the self-attention mechanism for predicting the emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are considered for predicting emotions (and expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie to, thereby, capture the relationships between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying self-attention first on vectors of features obtained from different modalities in each movie segment and then on the feature representations of all subsequent (temporal) movie segments. We extensively trained and validated our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting the emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately. In addition, the Feature AAN outperformed other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performed better than the baseline models when predicting the valence dimension.

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Spatiotemporal Attention Networks for Wind Power Forecasting

Cited by 23 publications

References 19 publications

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

AttendAffectNet–Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention

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