Wind Power Prediction Based on LS-SVM Model with Error Correction Article Swipe
Yagang Zhang
,
Pengyu Wang
,
Tongguang Ni
,
Penglai Cheng
,
Shuang Lei
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.4316/aece.2017.01001
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.4316/aece.2017.01001
As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM) model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM). Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4316/aece.2017.01001
- OA Status
- gold
- Cited By
- 67
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2592345322
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2592345322Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4316/aece.2017.01001Digital Object Identifier
- Title
-
Wind Power Prediction Based on LS-SVM Model with Error CorrectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
-
Yagang Zhang, Pengyu Wang, Tongguang Ni, Penglai Cheng, Shuang LeiList of authors in order
- Landing page
-
https://doi.org/10.4316/aece.2017.01001Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.4316/aece.2017.01001Direct OA link when available
- Concepts
-
Renewable energy, Wind power, Support vector machine, Wind power forecasting, Computer science, Power (physics), Environmental economics, Meteorology, Environmental science, Engineering, Electric power system, Machine learning, Economics, Electrical engineering, Geography, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
67Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 4, 2023: 10, 2022: 6, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.world's | 7 |
| abstract_inverted_index.(LS-SVM) | 79 |
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| abstract_inverted_index.However, | 48 |
| abstract_inverted_index.LS-SVM). | 155 |
| abstract_inverted_index.absolute | 168 |
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| cited_by_percentile_year.max | 100 |
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| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
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| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.96147444 |
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