An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM
ABSTRACT Accurate load forecasting can scientifically guide the optimal operation and scheduling of urban power grids. This study introduces an enhanced power load forecasting algorithm, integrating slime mould algorithm (SMA) and long short‐time memory (LSTM) to effectively address the hyperparamet...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.70100 |
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| _version_ | 1849722765794344960 |
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| author | Wei Liu Fan Hua Yongping Cui Yangchao Xu Han Liu |
| author_facet | Wei Liu Fan Hua Yongping Cui Yangchao Xu Han Liu |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | ABSTRACT Accurate load forecasting can scientifically guide the optimal operation and scheduling of urban power grids. This study introduces an enhanced power load forecasting algorithm, integrating slime mould algorithm (SMA) and long short‐time memory (LSTM) to effectively address the hyperparameter challenges associated with LSTM, while also applying variational modal decomposition (VMD) to load forecasting. In the data processing stage, the Bisecting Kmeans algorithm (Bi‐Kmeans) is used to identify the outliers of the measured load data, then the random forest (RF) is used to correct them, which determines reasonable load data. In the data analysis stage, the processed load data undergoes VMD, yielding components with distinct central frequencies, and the components of different frequencies are determined according to their energy values. In the prediction stage, an optimized LSTM using SMA is proposed to predict different frequency components separately, and the prediction results of multiple components are inversely reconfigured to obtain the load prediction results. Case studies demonstrate that the proposed algorithm outperforms other power load forecasting methods in prediction accuracy. |
| format | Article |
| id | doaj-art-4114dd30f61440889e7e9e6ee3c9f6c0 |
| institution | DOAJ |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-4114dd30f61440889e7e9e6ee3c9f6c02025-08-20T03:11:14ZengWileyEnergy Science & Engineering2050-05052025-06-011363243325310.1002/ese3.70100An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTMWei Liu0Fan Hua1Yongping Cui2Yangchao Xu3Han Liu4School of Automation Nanjing University of Science and Technology Nanjing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaState Grid Zhejiang Electric Power Company Shaoxing Power Supply Company Shaoxing ChinaSchool of Automation Nanjing University of Science and Technology Nanjing ChinaABSTRACT Accurate load forecasting can scientifically guide the optimal operation and scheduling of urban power grids. This study introduces an enhanced power load forecasting algorithm, integrating slime mould algorithm (SMA) and long short‐time memory (LSTM) to effectively address the hyperparameter challenges associated with LSTM, while also applying variational modal decomposition (VMD) to load forecasting. In the data processing stage, the Bisecting Kmeans algorithm (Bi‐Kmeans) is used to identify the outliers of the measured load data, then the random forest (RF) is used to correct them, which determines reasonable load data. In the data analysis stage, the processed load data undergoes VMD, yielding components with distinct central frequencies, and the components of different frequencies are determined according to their energy values. In the prediction stage, an optimized LSTM using SMA is proposed to predict different frequency components separately, and the prediction results of multiple components are inversely reconfigured to obtain the load prediction results. Case studies demonstrate that the proposed algorithm outperforms other power load forecasting methods in prediction accuracy.https://doi.org/10.1002/ese3.70100Bisecting Kmeanslong short‐term memorypower load forecastslime mould algorithm |
| spellingShingle | Wei Liu Fan Hua Yongping Cui Yangchao Xu Han Liu An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM Energy Science & Engineering Bisecting Kmeans long short‐term memory power load forecast slime mould algorithm |
| title | An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM |
| title_full | An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM |
| title_fullStr | An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM |
| title_full_unstemmed | An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM |
| title_short | An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM |
| title_sort | optimized power load forecasting algorithm based on vmd sma lstm |
| topic | Bisecting Kmeans long short‐term memory power load forecast slime mould algorithm |
| url | https://doi.org/10.1002/ese3.70100 |
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