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基于双重注意力LSTM的可解释海上风电出力预测
作者:
作者单位:

1.上海电力大学电气工程学院,上海市 200090;2.新南威尔士大学电气工程与通信学院,悉尼 2052,澳大利亚

摘要:

海上风电面临复杂多变气象和海况条件的深度耦合影响,导致其出力预测精度有待提升。同时,预测模型的“黑盒”结构导致出力预测结果在工程应用中可信度偏低。针对上述问题,提出一种基于双重注意力长短期记忆(DALSTM)网络的超短期海上风电出力预测模型。在长短期记忆神经网络的基础上,引入特征空间和时序双重注意力机制,动态挖掘海上风电出力与输入特征间的潜在相关性,并从特征和时间2个方面获得重要性量度,在一定程度上实现了模型的可解释性。最后,基于东海大桥海上风电场数据采集与监控数据进行仿真验证。结果表明:所提DALSTM网络模型能够对海上风电出力进行有效的超短期预测,相比传统预测模型具有更高的预测精度和稳定性,同时具有合理的可解释性。

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基金项目:

上海市教育委员会科研创新计划资助项目(2021-01-07-00-07-E00122)。

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作者简介:


Interpretable Offshore Wind Power Output Forecasting Based on Dual-stage Attention Long Short-Term Memory
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Abstract:

Offshore wind power is faced with the deep coupling effect of complex and changeable meteorological and sea conditions, which leads to the accuracy of output forecasting to be improved. At the same time, the “black box” structure of the forecasting model results in the low reliability of output forecasting results in engineering applications. To address the above problems, an ultra-short-term offshore wind power output forecasting model is proposed based on dual-stage attention long short-term memory (DALSTM) network. Based on the long short-term memory neural network, the dual-stage attention mechanism of feature space and time series is introduced to dynamically explore the potential correlations between the offshore wind power output the input features. The importance evaluation is carried out from feature and time to improve the interpretability of the proposed DALSTM model. Finally, simulations are conducted on the supervisory control and data acquisition data collected from the Donghai Bridge offshore wind farm. The results show that the proposed DALSTM model can effectively forecast the ultra-short-term offshore wind power output, and has higher forecasting accuracy and stability than the traditional forecasting models, as well as reasonable interpretation.

Keywords:

Foundation:
This work is supported by Scientific Research and Innovation Program of Shanghai Education Commission (No. 2021-01-07-00-07-E00122).
引用本文
[1]苏向敬,周汶鑫,李超杰,等.基于双重注意力LSTM的可解释海上风电出力预测[J/OL].电力系统自动化,http://doi. org/10.7500/AEPS20210427004.
SU Xiangjing, ZHOU Wenxin, LI Chaojie, et al. Interpretable Offshore Wind Power Output Forecasting Based on Dual-stage Attention Long Short-Term Memory[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20210427004.
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  • 收稿日期:2021-04-27
  • 最后修改日期:2022-01-07
  • 录用日期:2021-11-04
  • 在线发布日期: 2022-01-14
  • 出版日期: