An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model
This paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. First, the method decomposes the original power load data and...
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2025-04-01
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| Online Access: | https://www.mdpi.com/1996-1073/18/9/2240 |
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| author | Jiawen You Huafeng Cai Dadian Shi Liwei Guo |
| author_facet | Jiawen You Huafeng Cai Dadian Shi Liwei Guo |
| author_sort | Jiawen You |
| collection | DOAJ |
| description | This paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. First, the method decomposes the original power load data and environmental parameter data using VMD to capture their multi-scale characteristics. Next, KPCA extracts nonlinear features and reduces the dimensionality of the decomposed modals to eliminate redundant information while retaining key features. The xLSTM network then models temporal dependencies to enhance the model’s memory capability and prediction accuracy. Finally, the Informer model processes long-sequence data to improve prediction efficiency. Experimental results demonstrate that the VMD–KPCA–xLSTM–Informer model achieves an average absolute percentage error (MAPE) as low as 2.432% and a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.9532 on dataset I, while, on dataset II, it attains a MAPE of 4.940% and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.8897. These results confirm that the method significantly improves the accuracy and stability of short-term power load forecasting, providing robust support for power system optimization. |
| format | Article |
| id | doaj-art-af2bc2b6a3c24d36815451e3df59abbb |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-af2bc2b6a3c24d36815451e3df59abbb2025-08-20T01:49:11ZengMDPI AGEnergies1996-10732025-04-01189224010.3390/en18092240An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer ModelJiawen You0Huafeng Cai1Dadian Shi2Liwei Guo3Normal School of Vocational Techniques, Hubei University of Technology, Wuhan 430068, ChinaSchool of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, ChinaDetroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, ChinaDetroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, ChinaThis paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. First, the method decomposes the original power load data and environmental parameter data using VMD to capture their multi-scale characteristics. Next, KPCA extracts nonlinear features and reduces the dimensionality of the decomposed modals to eliminate redundant information while retaining key features. The xLSTM network then models temporal dependencies to enhance the model’s memory capability and prediction accuracy. Finally, the Informer model processes long-sequence data to improve prediction efficiency. Experimental results demonstrate that the VMD–KPCA–xLSTM–Informer model achieves an average absolute percentage error (MAPE) as low as 2.432% and a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.9532 on dataset I, while, on dataset II, it attains a MAPE of 4.940% and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.8897. These results confirm that the method significantly improves the accuracy and stability of short-term power load forecasting, providing robust support for power system optimization.https://www.mdpi.com/1996-1073/18/9/2240short-term electricity load forecastingvariational-mode decompositionhybrid neural network |
| spellingShingle | Jiawen You Huafeng Cai Dadian Shi Liwei Guo An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model Energies short-term electricity load forecasting variational-mode decomposition hybrid neural network |
| title | An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model |
| title_full | An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model |
| title_fullStr | An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model |
| title_full_unstemmed | An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model |
| title_short | An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model |
| title_sort | improved short term electricity load forecasting method the vmd kpca xlstm informer model |
| topic | short-term electricity load forecasting variational-mode decomposition hybrid neural network |
| url | https://www.mdpi.com/1996-1073/18/9/2240 |
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