论文标题
AC-OPF解决方案推理的深度学习体系结构
Deep learning architectures for inference of AC-OPF solutions
论文作者
论文摘要
我们提出了用于推断AC-OPF解决方案的神经网络（NN）架构之间的系统比较。使用完全连接的NN作为基线，我们通过构造图形域中的电网格的抽象表示来证明在模型中利用网络拓扑的功效，以构建卷积和图NNS。比较了NN体系结构的性能，以进行回归（预测最佳发电机设定点）和分类（预测约束的活动集合）设置。还提出了获得最佳解决方案的计算收益。
We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain, for both convolutional and graph NNs. The performance of the NN architectures is compared for regression (predicting optimal generator set-points) and classification (predicting the active set of constraints) settings. Computational gains for obtaining optimal solutions are also presented.
