Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental Training Article Swipe
Guangyu Chen
,
Yijie Wu
,
Li Yang
,
Ke Xu
,
Gang Lin
,
Yangfei Zhang
,
Yuzhuo Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15197353
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15197353
In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en15197353
- https://www.mdpi.com/1996-1073/15/19/7353/pdf?version=1665392911
- OA Status
- gold
- Cited By
- 4
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4303699858
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- OpenAlex ID
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https://openalex.org/W4303699858Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en15197353Digital Object Identifier
- Title
-
Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental TrainingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-06Full publication date if available
- Authors
-
Guangyu Chen, Yijie Wu, Li Yang, Ke Xu, Gang Lin, Yangfei Zhang, Yuzhuo ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/en15197353Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/15/19/7353/pdf?version=1665392911Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1996-1073/15/19/7353/pdf?version=1665392911Direct OA link when available
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Computer science, Term (time), Data set, Set (abstract data type), Data mining, Process (computing), Key (lock), Artificial intelligence, Machine learning, Computer security, Quantum mechanics, Operating system, Physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 3, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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