Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.
Department of Energy Distribution and High Voltage Engineering, Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, Germany.
Sensors (Basel). 2023 Jan 12;23(2):901. doi: 10.3390/s23020901.
The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.
大规模的可再生能源和储能在电网中的安装，由于发电过剩，可能会导致双向潮流时的能源消耗波动。为了确保电网的安全和可靠性，需要高质量的双向潮流预测。然而，由于潮流的不断变化特性和天气对可再生能源发电的影响，双向潮流预测仍然是一个挑战。为了克服这些挑战，我们提出了两种最流行的基于卷积神经网络（CNN）和长期记忆（LSTM）组合的混合深度学习（HDL）模型，用于预测所研究的网络集群中的潮流。在我们的方法中，CNN-LSTM 和 LSTM-CNN 模型使用两个不同大小和包含参数的数据集进行训练。目的是观察数据集的大小和额外的天气数据是否会影响所提出的模型预测潮流的性能。结果表明，在某些条件下，两种提出的模型都可以达到较小的误差。而数据集的大小和参数会影响 HDL 模型的训练时间和准确性。
