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Superposition Graph Neural Network for offshore wind power prediction
Future Generation Computer Systems ( IF 7.5 ) Pub Date : 2020-06-20 , DOI: 10.1016/j.future.2020.06.024
Mei Yu , Zhuo Zhang , Xuewei Li , Jian Yu , Jie Gao , Zhiqiang Liu , Bo You , Xiaoshan Zheng , Ruiguo Yu

Wind power prediction plays an important role in its utilization. Currently, in machine learning methods and other traditional methods, the prediction is always based on the time series of data nodes, and sometimes wind turbines near the predicted nodes are also applied. These methods have limitations in the utilization of the spatial features of the entire wind farm, and can only be used to predict a single wind turbine. Offshore wind farm data is more difficult to predict due to the more dispersed distribution of wind turbines and the intermittent nature of offshore winds. We proposed a data integration method, which can connect all wind turbines in a certain range of wind farms by their geographical locations and other related information to form a graph(one type of data structure), then superimpose these graphs in a certain period of time. Then, we proposed the SGNN(Superposition Graph Neural Network) for feature extraction, which can maximize the use of spatial and temporal features for prediction. In the four offshore wind farms used in experiments, the mean square error (MSE) of the method is reduced by 9.80% to 22.53% compared with current-advanced methods, and the prediction stability of the method has also been greatly improved.



中文翻译:

叠加图神经网络在海上风电预测中的应用

风能预测在其利用中起着重要作用。当前,在机器学习方法和其他传统方法中,预测总是基于数据节点的时间序列,并且有时还应用靠近预测节点的风力涡轮机。这些方法在利用整个风电场的空间特征方面有局限性,只能用于预测单个风力涡轮机。由于风力涡轮机的分布更加分散以及海上风的间歇性,海上风电场的数据更加难以预测。我们提出了一种数据集成方法,该方法可以通过一定地理位置的风力涡轮机的地理位置和其他相关信息将所有风力涡轮机连接起来,形成一个图(一种数据结构),然后在一定的时间段内将这些图叠加。然后,我们提出了用于特征提取的SGNN(叠加图神经网络),它可以最大限度地利用空间和时间特征进行预测。在实验中使用的四个海上风电场中,该方法的均方误差(MSE)与当前方法相比降低了9.80%至22.53%,并且该方法的预测稳定性也得到了极大的提高。

更新日期:2020-06-20
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