Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism
A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follow...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/12/4616 |
| _version_ | 1827737542094487552 |
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| author | Guanchen Geng Yu He Jing Zhang Tingxiang Qin Bin Yang |
| author_facet | Guanchen Geng Yu He Jing Zhang Tingxiang Qin Bin Yang |
| author_sort | Guanchen Geng |
| collection | DOAJ |
| description | A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model’s prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability. |
| first_indexed | 2024-03-11T02:31:22Z |
| format | Article |
| id | doaj.art-0a0a78c88d594eb6ae8dedac55726997 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T02:31:22Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0a0a78c88d594eb6ae8dedac557269972023-11-18T10:11:49ZengMDPI AGEnergies1996-10732023-06-011612461610.3390/en16124616Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention MechanismGuanchen Geng0Yu He1Jing Zhang2Tingxiang Qin3Bin Yang4College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaPowerChina Guizhou Engineering Co., Ltd., Guiyang 550001, ChinaPowerChina Guizhou Engineering Co., Ltd., Guiyang 550001, ChinaA new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model’s prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability.https://www.mdpi.com/1996-1073/16/12/4616variational mode decomposition (VMD)time convolution network (TCN)attention mechanismshort-term load forecastingparticle swarm optimization (PSO) |
| spellingShingle | Guanchen Geng Yu He Jing Zhang Tingxiang Qin Bin Yang Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism Energies variational mode decomposition (VMD) time convolution network (TCN) attention mechanism short-term load forecasting particle swarm optimization (PSO) |
| title | Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism |
| title_full | Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism |
| title_fullStr | Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism |
| title_full_unstemmed | Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism |
| title_short | Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism |
| title_sort | short term power load forecasting based on pso optimized vmd tcn attention mechanism |
| topic | variational mode decomposition (VMD) time convolution network (TCN) attention mechanism short-term load forecasting particle swarm optimization (PSO) |
| url | https://www.mdpi.com/1996-1073/16/12/4616 |
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