التفاصيل البيبلوغرافية
| العنوان: |
Short-Term Power Load Prediction Based on VMD-SG-LSTM |
| المؤلفون: |
Qiu Sun, Huafeng Cai |
| المصدر: |
IEEE Access, Vol 10, Pp 102396-102405 (2022) |
| بيانات النشر: |
IEEE, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Power load prediction, variational mode decomposition, intrinsic mode function, S-G filtering, long short-term memory network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Power load prediction plays an important role in the safety and stability of national power system. However, due to the nonlinear and multi-frequency characteristics of the power system itself, power load prediction is difficult. To address this problem, we propose a short-term power load prediction model based on variational mode decomposition (VMD). First, original data are decomposed into intrinsic mode function (IMF) of different frequencies using the VMD algorithm, and the decomposed sub-functions are reconstructed. After smoothing the reconstructed data by Savitzky-Golay (S-G) filtering algorithm, the change trend of raw data (CTRD) is obtained. Then, IMF, CTRD and raw data are used as inputs to predict short-term power load by long short-term memory network (LSTM). Finally, the proposed prediction model is compared with the other two groups of prediction models. The results show that the proposed VMD-SG-LSTM prediction model has high fitting ability and high prediction accuracy, and is an effective method for short-term power load prediction. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9889697/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3206486 |
| URL الوصول: |
https://doaj.org/article/f61b95af1d5142098dd15a02a4f55a74 |
| رقم الانضمام: |
edsdoj.f61b95af1d5142098dd15a02a4f55a74 |
| قاعدة البيانات: |
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