New Energy Power Prediction and Warning Based on Multi-source Prediction and Scene Classification Recognition Article Swipe
Ruizeng Wei
,
Siyu Hu
,
Fan Yang
,
Guorui Shi
,
Daqian Zhang
,
Lintao Zhang
,
Xu Fang
,
Xiao Tan
,
Junmin Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2023.09.055
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2023.09.055
This article proposes a new method for predicting and warning of new energy electricity based on a multi-source prediction data and scene classification recognition algorithm. The study uses ECMWF, GFS, and DERF2.0 multi-source prediction data to improve the accuracy of offshore wind power prediction and disaster warning. In order to overcome the limitations of traditional prediction methods, this study adopts a scene classification recognition method. Based on meteorological, power grid, and environmental data, the study uses a scene recognition algorithm to establish three types of factor feature extraction models and combines them with fault information to establish a wind power prediction and warning model. This model can achieve the prediction of wind power and warn of possible power grid faults under meteorological disasters. Case analysis shows that this method significantly improves the accuracy of new energy electricity prediction and meteorological disaster warning. This study emphasizes the importance of multi-source prediction data and scene classification recognition methods in improving offshore wind power prediction and other new energy fields. Additionally, this method has the potential to make valuable contributions to the renewable energy industry.
Related Topics
Concepts
Computer science
Wind power
Offshore wind power
Warning system
Data mining
Renewable energy
Wind power forecasting
Predictive modelling
Grid
Electricity
Artificial intelligence
Power (physics)
Machine learning
Electric power system
Telecommunications
Mathematics
Physics
Electrical engineering
Engineering
Geometry
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2023.09.055
- OA Status
- diamond
- Cited By
- 3
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387490702
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387490702Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2023.09.055Digital Object Identifier
- Title
-
New Energy Power Prediction and Warning Based on Multi-source Prediction and Scene Classification RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Ruizeng Wei, Siyu Hu, Fan Yang, Guorui Shi, Daqian Zhang, Lintao Zhang, Xu Fang, Xiao Tan, Junmin HuangList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2023.09.055Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2023.09.055Direct OA link when available
- Concepts
-
Computer science, Wind power, Offshore wind power, Warning system, Data mining, Renewable energy, Wind power forecasting, Predictive modelling, Grid, Electricity, Artificial intelligence, Power (physics), Machine learning, Electric power system, Telecommunications, Mathematics, Physics, Electrical engineering, Engineering, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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