CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany
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-qualit...
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| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/901 |
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| author | Fachrizal Aksan Yang Li Vishnu Suresh Przemysław Janik |
| author_facet | Fachrizal Aksan Yang Li Vishnu Suresh Przemysław Janik |
| author_sort | Fachrizal Aksan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:17:28Z |
| format | Article |
| id | doaj.art-9f0f2936e1ae443ea575bf0b54405573 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:28Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9f0f2936e1ae443ea575bf0b544055732023-12-01T00:29:31ZengMDPI AGSensors1424-82202023-01-0123290110.3390/s23020901CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast GermanyFachrizal Aksan0Yang Li1Vishnu Suresh2Przemysław Janik3Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Energy Distribution and High Voltage Engineering, Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, GermanyFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandThe 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.https://www.mdpi.com/1424-8220/23/2/901CNN-LSTMLSTM-CNNpower flow predictionnetwork cluster |
| spellingShingle | Fachrizal Aksan Yang Li Vishnu Suresh Przemysław Janik CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany Sensors CNN-LSTM LSTM-CNN power flow prediction network cluster |
| title | CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany |
| title_full | CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany |
| title_fullStr | CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany |
| title_full_unstemmed | CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany |
| title_short | CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany |
| title_sort | cnn lstm vs lstm cnn to predict power flow direction a case study of the high voltage subnet of northeast germany |
| topic | CNN-LSTM LSTM-CNN power flow prediction network cluster |
| url | https://www.mdpi.com/1424-8220/23/2/901 |
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