Probabilistic Forecasting of Offshore Wind Power Based on Dual-stage Attentional LSTM and Joint Quantile Loss Function

doi:10.11930/j.issn.1004-9649.202212011

Abstract

Abstract:

Probabilistic prediction of offshore wind power is not high in accuracy due to the predetermined threshold limitation of the traditional feature correlation method and the magnitude difference of the quantile loss in each quantile loss. To improve the probabilistic prediction accuracy, a multi-task joint quantile loss-based dual-attention probabilistic prediction model (MT-DALSTM) is proposed. Firstly, a feature and temporal dual attention mechanism is introduced to mine the correlation and temporal dependence among features, and attention weights are given to key features and time point information to improve the accuracy of power prediction. Secondly, during model training, the multi-task joint quantile loss based on task uncertainty is used to improve the final prediction results by dynamically adjusting the proportion of each loss weight. Finally, the simulation validation results based on the real data from the Donghai Bridge offshore wind farm show that the proposed method has significant improvement in sharpness, reliability and comprehensive performance indexes compared to the existing wind power probabilistic prediction studies, which verifies the effectiveness of the model in improving the prediction accuracy.

Key words: offshore wind power, probabilistic prediction, attention mechanism, attention weight, feature relevance, quantile regression

Xiangjing SU, Haibo YU, Yang FU, Shuxin TIAN, Haiyu LI, Fuhai GENG. Probabilistic Forecasting of Offshore Wind Power Based on Dual-stage Attentional LSTM and Joint Quantile Loss Function[J]. Electric Power, 2023, 56(11): 10-19.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202212011

https://www.electricpower.com.cn/EN/Y2023/V56/I11/10

Figures/Tables 13

Fig.1 LSTM structure

Fig.2 Feature attention structure

Fig.3 Temporal attention structure

Fig.4 Quantile forecasting structure

Fig.5 Forecast flow chart

Fig.6 Power predictions by different models

Table 1 Comparison of probabilistic predicting results

预测模型	S_AW/kW	S_CRPS
DQR	483.23	94.94
LSTM-QR	457.44	89.54
DALSTM	407.28	71.41
MT-DALSTM	372.37	64.23

Fig.7 ACD of different models

Fig.8 CRPS of different models

Fig.9 Feature attention weights

Fig.10 Comparison of pitch characteristics

Table 2 Comparison of pitch characteristics probabilistic predicting results

预测模型	S_AW/kW	S_CRPS
MT-DALSTM（无变桨特征）	394.46	69.17
MT-DALSTM	372.37	64.23

Fig.11 Temporal attention weights

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