A Scheme for Charging Load Prediction of EV Based on Fuzzy Theory Article Swipe
Sike Wang
,
Liansong Yu
,
Peng Cao
,
Huafeng Hu
,
Bo Pang
,
Wei Luo
,
Xiaohu Ge
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia231222
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3233/faia231222
With the development of the electric vehicle industry, the increasing charging load of electric vehicles has brought enormous pressure to urban distribution networks, affecting their safety and efficiency. Therefore, the power system needs a better load forecasting model to predict the load value more accurately. Considering that traditional electric vehicle charging load prediction models still have many shortcomings, this article uses fuzzy theory to deal with uncertain influencing factors and combines fuzzy clustering method to analyze the charging habits of local residents. In order to make the power load prediction results more effective and reliable, this paper proposes a fuzzy neural network prediction model that takes into account user habits. The key influencing factors are fuzzy processed, and the fuzzy c-means clustering method is introduced for mining. The law of charging time for most car owners is analyzed. The superiority of the prediction model incorporating fuzzy theory was verified through comparative experiments. The accuracy of the final time-sharing prediction is above 90%.
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.3233/faia231222
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231222
- OA Status
- hybrid
- Cited By
- 2
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390815516
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390815516Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3233/faia231222Digital Object Identifier
- Title
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A Scheme for Charging Load Prediction of EV Based on Fuzzy TheoryWork title
- Type
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-12Full publication date if available
- Authors
-
Sike Wang, Liansong Yu, Peng Cao, Huafeng Hu, Bo Pang, Wei Luo, Xiaohu GeList of authors in order
- Landing page
-
https://doi.org/10.3233/faia231222Publisher landing page
- PDF URL
-
https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231222Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231222Direct OA link when available
- Concepts
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Fuzzy logic, Computer science, Electric vehicle, Fuzzy clustering, Cluster analysis, Key (lock), Artificial neural network, Scheme (mathematics), Data mining, Electric power system, Power (physics), Artificial intelligence, Mathematics, Computer security, Quantum mechanics, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.their | 24 |
| abstract_inverted_index.urban | 20 |
| abstract_inverted_index.value | 42 |
| abstract_inverted_index.better | 34 |
| abstract_inverted_index.habits | 78 |
| abstract_inverted_index.method | 73, 122 |
| abstract_inverted_index.models | 53 |
| abstract_inverted_index.neural | 100 |
| abstract_inverted_index.owners | 135 |
| abstract_inverted_index.safety | 25 |
| abstract_inverted_index.system | 31 |
| abstract_inverted_index.theory | 62, 146 |
| abstract_inverted_index.account | 107 |
| abstract_inverted_index.analyze | 75 |
| abstract_inverted_index.article | 59 |
| abstract_inverted_index.brought | 16 |
| abstract_inverted_index.c-means | 120 |
| abstract_inverted_index.factors | 68, 113 |
| abstract_inverted_index.habits. | 109 |
| abstract_inverted_index.mining. | 126 |
| abstract_inverted_index.network | 101 |
| abstract_inverted_index.predict | 39 |
| abstract_inverted_index.results | 90 |
| abstract_inverted_index.through | 149 |
| abstract_inverted_index.vehicle | 6, 49 |
| abstract_inverted_index.accuracy | 153 |
| abstract_inverted_index.charging | 10, 50, 77, 130 |
| abstract_inverted_index.combines | 70 |
| abstract_inverted_index.electric | 5, 13, 48 |
| abstract_inverted_index.enormous | 17 |
| abstract_inverted_index.pressure | 18 |
| abstract_inverted_index.proposes | 97 |
| abstract_inverted_index.vehicles | 14 |
| abstract_inverted_index.verified | 148 |
| abstract_inverted_index.affecting | 23 |
| abstract_inverted_index.analyzed. | 137 |
| abstract_inverted_index.effective | 92 |
| abstract_inverted_index.industry, | 7 |
| abstract_inverted_index.networks, | 22 |
| abstract_inverted_index.reliable, | 94 |
| abstract_inverted_index.uncertain | 66 |
| abstract_inverted_index.Therefore, | 28 |
| abstract_inverted_index.clustering | 72, 121 |
| abstract_inverted_index.increasing | 9 |
| abstract_inverted_index.introduced | 124 |
| abstract_inverted_index.prediction | 52, 89, 102, 142, 158 |
| abstract_inverted_index.processed, | 116 |
| abstract_inverted_index.residents. | 81 |
| abstract_inverted_index.Considering | 45 |
| abstract_inverted_index.accurately. | 44 |
| abstract_inverted_index.comparative | 150 |
| abstract_inverted_index.development | 2 |
| abstract_inverted_index.efficiency. | 27 |
| abstract_inverted_index.forecasting | 36 |
| abstract_inverted_index.influencing | 67, 112 |
| abstract_inverted_index.superiority | 139 |
| abstract_inverted_index.traditional | 47 |
| abstract_inverted_index.distribution | 21 |
| abstract_inverted_index.experiments. | 151 |
| abstract_inverted_index.time-sharing | 157 |
| abstract_inverted_index.incorporating | 144 |
| abstract_inverted_index.shortcomings, | 57 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.92545193 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |