Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of th...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/493 |
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| author | Peihao Tang Zhen Li Xuanlin Wang Xueping Liu Peng Mou |
| author_facet | Peihao Tang Zhen Li Xuanlin Wang Xueping Liu Peng Mou |
| author_sort | Peihao Tang |
| collection | DOAJ |
| description | Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R<sup>2</sup> value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data. |
| format | Article |
| id | doaj-art-c42407788a7344c6bfc60872c520d224 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c42407788a7344c6bfc60872c520d2242025-01-24T13:49:07ZengMDPI AGSensors1424-82202025-01-0125249310.3390/s25020493Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGANPeihao Tang0Zhen Li1Xuanlin Wang2Xueping Liu3Peng Mou4Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDivision of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDivision of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDivision of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaPredicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R<sup>2</sup> value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data.https://www.mdpi.com/1424-8220/25/2/493data augmentationTimeGANdeep learningtime series |
| spellingShingle | Peihao Tang Zhen Li Xuanlin Wang Xueping Liu Peng Mou Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN Sensors data augmentation TimeGAN deep learning time series |
| title | Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN |
| title_full | Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN |
| title_fullStr | Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN |
| title_full_unstemmed | Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN |
| title_short | Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN |
| title_sort | time series data augmentation for energy consumption data based on improved timegan |
| topic | data augmentation TimeGAN deep learning time series |
| url | https://www.mdpi.com/1424-8220/25/2/493 |
| work_keys_str_mv | AT peihaotang timeseriesdataaugmentationforenergyconsumptiondatabasedonimprovedtimegan AT zhenli timeseriesdataaugmentationforenergyconsumptiondatabasedonimprovedtimegan AT xuanlinwang timeseriesdataaugmentationforenergyconsumptiondatabasedonimprovedtimegan AT xuepingliu timeseriesdataaugmentationforenergyconsumptiondatabasedonimprovedtimegan AT pengmou timeseriesdataaugmentationforenergyconsumptiondatabasedonimprovedtimegan |