Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems
Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems
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DOI：
10.1609/aaai.v35i17.17799
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发表时间：
2021-05
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影响因子：
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通讯作者：
A. M. Karimi;Yinghui Wu;Mehmet Koyutürk;R. French
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文献类型：
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作者：
A. M. Karimi;Yinghui Wu;Mehmet Koyutürk;R. French
近年来，大量的光伏（PV）系统已经被添加到电网以及作为离网系统安装。这一趋势表明，未来光伏系统的部署将继续上升。因此，准确预测光伏性能对光伏系统的可靠性至关重要。由于光伏系统的功率输出具有复杂的非线性变化，预测光伏功率是一项重要的任务。这种可变性影响电力系统网络的稳定性和规划，并且准确预测PV系统的性能可以减少PV操作期间引起的不确定性。在这项工作中，我们利用光伏发电预测的发电厂之间的空间和时间的一致性。我们的方法是出于观察，在一个地区的发电厂经历类似的环境暴露。因此，一个发电厂的性能可以帮助改善该地区其他发电厂的功率值的预测。我们利用光伏发电厂之间的关系，建立一个时空图神经网络（st-GNN）和训练机器学习模型来预测光伏发电功率。从316个系统的网络的大规模数据的计算实验表明，时空预测的光伏发电性能显着优于模型，仅适用于时间卷积孤立的系统或节点。而且，未来预测时间越长，时空预测与仅应用时间卷积时的孤立系统预测之间的差异进一步增大。
In recent years, a large number of photovoltaic (PV) systems have been added to the electrical grid as well as installed as off-grid systems. The trend suggests that the deployment of PV systems will continue to rise in the future. Thus, accurate forecasting of PV performance is critical for the reliability of PV systems. Due to the complex non-linear variability in power output of the PV systems, forecasting PV power is a non-trivial task. This variability affects the stability and planning of a power system network, and accurate forecasting of the performance of the PV system can reduce the uncertainty caused during PV operation. In this work, we leverage spatial and temporal coherence among the power plants for PV power forecasting. Our approach is motivated by the observation that power plants in a region undergo similar environmental exposure. Thus, one power plant’s performance can help improve the forecast of other power plants' power values in the region. We utilize the relationship between PV plants to build a spatiotemporal graph neural network (st-GNN) and train machine learning models to forecast the PV power. The computational experiments on large-scale data from a network of 316 systems show that spatiotemporal forecasting of PV power performs significantly better than a model that only applies temporal convolution to isolated systems or nodes. Furthermore, the longer the future forecast time, the difference between the spatiotemporal forecasting and the isolated system forecast when only temporal convolution is applied increases further.