MRGS-LSTM: a novel multi-site wind speed prediction approach with spatio-temporal correlation

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-08-29 DOI:10.3389/fenrg.2024.1427587
Yueguang Zhou, Xiuxiang Fan
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Abstract

The wind energy industry is witnessing a new era of extraordinary growth as the demand for renewable energy continues to grow. However, accurately predicting wind speed remains a significant challenge due to its high fluctuation and randomness. These difficulties hinder effective wind farm management and integration into the power grid. To address this issue, we propose the MRGS-LSTM model to improve the accuracy and reliability of wind speed prediction results, which considers the complex spatio-temporal correlations between features at multiple sites. First, mRMR-RF filters the input multidimensional meteorological variables and computes the feature subset with minimum information redundancy. Second, the feature map topology is constructed by quantifying the spatial distance distribution of the multiple sites and the maximum mutual information coefficient among the features. On this basis, the GraphSAGE framework is used to sample and aggregate the feature information of neighboring sites to extract spatial feature vectors. Then, the spatial feature vectors are input into the long short-term memory (LSTM) model after sliding window sampling. The LSTM model learns the temporal features of wind speed data to output the predicted results of the spatio-temporal correlation at each site. Finally, through the simulation experiments based on real historical data from the Roscoe Wind Farm in Texas, United States, we prove that our model MRGS-LSTM improves the performance of MAE by 15.43%–27.97% and RMSE by 12.57%–25.40% compared with other models of the same type. The experimental results verify the validity and superiority of our proposed model and provide a more reliable basis for the scheduling and optimization of wind farms.
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MRGS-LSTM:一种具有时空相关性的新型多站点风速预测方法
随着对可再生能源需求的不断增长,风能产业正迎来一个非凡增长的新时代。然而,由于风速的高波动性和随机性,准确预测风速仍然是一项重大挑战。这些困难阻碍了风电场的有效管理和并入电网。为解决这一问题,我们提出了 MRGS-LSTM 模型来提高风速预测结果的准确性和可靠性,该模型考虑了多个站点特征之间复杂的时空相关性。首先,mRMR-RF 对输入的多维气象变量进行过滤,计算出信息冗余最小的特征子集。其次,通过量化多个站点的空间距离分布和特征间的最大互信息系数,构建特征图拓扑结构。在此基础上,利用 GraphSAGE 框架对相邻站点的特征信息进行采样和聚合,提取空间特征向量。然后,经过滑动窗口采样,将空间特征向量输入长短期记忆(LSTM)模型。LSTM 模型学习风速数据的时间特征,输出各站点的时空相关性预测结果。最后,通过基于美国德克萨斯州罗斯科风电场真实历史数据的仿真实验,我们证明了与其他同类型模型相比,我们的 MRGS-LSTM 模型的 MAE 性能提高了 15.43%-27.97%,RMSE 提高了 12.57%-25.40%。实验结果验证了我们提出的模型的有效性和优越性,为风电场的调度和优化提供了更可靠的依据。
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来源期刊
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
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