Research on Photovoltaic Power Prediction Method Based on Dynamic Similar Selection and Bidirectional Gated Recurrent Unit Article Swipe
Qinghong Wang
,
Longhao Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/adts.202401423
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/adts.202401423
Photovoltaic (PV) power generation is vital for sustainable energy development, yet its inherent randomness and volatility challenge grid stability. Accurate short‐term PV power prediction is essential for reliable operation. This paper proposes an integrated prediction method combining dynamic similar selection (DSS), variational mode decomposition (VMD), bidirectional gated recurrent unit (BiGRU), and an improved sparrow search algorithm (ISSA). First, DSS selects training data based on local meteorological similarity, reducing randomness interference. VMD then decomposes PV power data into smooth components, mitigating volatility. The Pearson correlation coefficient is used to filter highly relevant meteorological variables, enhancing input quality. BiGRU captures temporal evolution patterns, with ISSA optimizing key parameters for robust forecasting. Validated on historical Australian PV data under diverse weather conditions, the proposed method effectively reduces randomness and volatility, significantly improving prediction accuracy and reliability. These advancements support stable PV power supply and efficient grid operation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/adts.202401423
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adts.202401423
- OA Status
- bronze
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408318315
All OpenAlex metadata
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https://openalex.org/W4408318315Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/adts.202401423Digital Object Identifier
- Title
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Research on Photovoltaic Power Prediction Method Based on Dynamic Similar Selection and Bidirectional Gated Recurrent UnitWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-08Full publication date if available
- Authors
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Qinghong Wang, Longhao LiList of authors in order
- Landing page
-
https://doi.org/10.1002/adts.202401423Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adts.202401423Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adts.202401423Direct OA link when available
- Concepts
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Photovoltaic system, Selection (genetic algorithm), Power (physics), Computer science, Unit (ring theory), Engineering, Machine learning, Mathematics, Electrical engineering, Physics, Quantum mechanics, Mathematics educationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.historical | 112 |
| abstract_inverted_index.integrated | 34 |
| abstract_inverted_index.mitigating | 80 |
| abstract_inverted_index.operation. | 29, 144 |
| abstract_inverted_index.optimizing | 104 |
| abstract_inverted_index.parameters | 106 |
| abstract_inverted_index.prediction | 24, 35, 130 |
| abstract_inverted_index.randomness | 14, 69, 125 |
| abstract_inverted_index.stability. | 19 |
| abstract_inverted_index.variables, | 93 |
| abstract_inverted_index.volatility | 16 |
| abstract_inverted_index.coefficient | 85 |
| abstract_inverted_index.components, | 79 |
| abstract_inverted_index.conditions, | 119 |
| abstract_inverted_index.correlation | 84 |
| abstract_inverted_index.effectively | 123 |
| abstract_inverted_index.similarity, | 67 |
| abstract_inverted_index.sustainable | 8 |
| abstract_inverted_index.variational | 42 |
| abstract_inverted_index.volatility, | 127 |
| abstract_inverted_index.volatility. | 81 |
| abstract_inverted_index.Photovoltaic | 1 |
| abstract_inverted_index.advancements | 135 |
| abstract_inverted_index.development, | 10 |
| abstract_inverted_index.forecasting. | 109 |
| abstract_inverted_index.reliability. | 133 |
| abstract_inverted_index.short‐term | 21 |
| abstract_inverted_index.bidirectional | 46 |
| abstract_inverted_index.decomposition | 44 |
| abstract_inverted_index.interference. | 70 |
| abstract_inverted_index.significantly | 128 |
| abstract_inverted_index.meteorological | 66, 92 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.02714423 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |