Enterprise power emission reduction technology based on the LSTM–SVM model Article Swipe
Ke Li
,
Meng Su
,
Qiang Liu
,
Bin Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1515/nleng-2025-0165
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1515/nleng-2025-0165
With the increasing emphasis on environmental protection in various regions, reducing electricity emissions for enterprises has become a popular development trend. The research aims to design a machine learning-based power data warning method to assist enterprises in reducing emissions and controlling costs. In terms of research methods, load type analysis is first conducted for industrial enterprises, followed by the introduction of long short-term memory (LSTM) networks to build a basic model for power data prediction. Support vector machines are used to optimize the model’s large sample requirements, and the two models are integrated to improve accuracy. Key research has found that the proposed model performed excellently. The minimum relative prediction error was 0.20%, the maximum error fluctuation was 0.78%, the accuracy was 12.85% higher than the LSTM model, and the recall was 11.60% higher. On 220 kV, the testing time was 17.5% faster than the data prediction model and 36.0% faster than the multi-task learning model, and the accuracy was always the best. Simulation experiments showed that after data warning, carbon emissions could be reduced by up to 48.26%, and electricity costs could be reduced by up to 60.48%. The machine learning-based power data warning method proposed in this study has important practical application value and can effectively help enterprises achieve emission reduction and cost control goals.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/nleng-2025-0165
- OA Status
- gold
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412974933
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412974933Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1515/nleng-2025-0165Digital Object Identifier
- Title
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Enterprise power emission reduction technology based on the LSTM–SVM modelWork 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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2025-01-01Full publication date if available
- Authors
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Ke Li, Meng Su, Qiang Liu, Bin ZhangList of authors in order
- Landing page
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https://doi.org/10.1515/nleng-2025-0165Publisher landing page
- 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://doi.org/10.1515/nleng-2025-0165Direct OA link when available
- Concepts
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Reduction (mathematics), Support vector machine, Computer science, Pattern recognition (psychology), Artificial intelligence, Power (physics), Machine learning, Mathematics, Physics, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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22Number 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.could | 173, 183 |
| abstract_inverted_index.error | 111, 116 |
| abstract_inverted_index.first | 52 |
| abstract_inverted_index.found | 100 |
| abstract_inverted_index.large | 85 |
| abstract_inverted_index.model | 71, 104, 148 |
| abstract_inverted_index.power | 30, 73, 193 |
| abstract_inverted_index.study | 200 |
| abstract_inverted_index.terms | 44 |
| abstract_inverted_index.value | 205 |
| abstract_inverted_index.(LSTM) | 65 |
| abstract_inverted_index.0.20%, | 113 |
| abstract_inverted_index.0.78%, | 119 |
| abstract_inverted_index.11.60% | 133 |
| abstract_inverted_index.12.85% | 123 |
| abstract_inverted_index.always | 161 |
| abstract_inverted_index.assist | 35 |
| abstract_inverted_index.become | 17 |
| abstract_inverted_index.carbon | 171 |
| abstract_inverted_index.costs. | 42 |
| abstract_inverted_index.design | 26 |
| abstract_inverted_index.faster | 143, 151 |
| abstract_inverted_index.goals. | 217 |
| abstract_inverted_index.higher | 124 |
| abstract_inverted_index.memory | 64 |
| abstract_inverted_index.method | 33, 196 |
| abstract_inverted_index.model, | 128, 156 |
| abstract_inverted_index.models | 91 |
| abstract_inverted_index.recall | 131 |
| abstract_inverted_index.sample | 86 |
| abstract_inverted_index.showed | 166 |
| abstract_inverted_index.trend. | 21 |
| abstract_inverted_index.vector | 77 |
| abstract_inverted_index.48.26%, | 179 |
| abstract_inverted_index.60.48%. | 189 |
| abstract_inverted_index.Support | 76 |
| abstract_inverted_index.achieve | 211 |
| abstract_inverted_index.control | 216 |
| abstract_inverted_index.higher. | 134 |
| abstract_inverted_index.improve | 95 |
| abstract_inverted_index.machine | 28, 191 |
| abstract_inverted_index.maximum | 115 |
| abstract_inverted_index.minimum | 108 |
| abstract_inverted_index.popular | 19 |
| abstract_inverted_index.reduced | 175, 185 |
| abstract_inverted_index.testing | 139 |
| abstract_inverted_index.various | 9 |
| abstract_inverted_index.warning | 32, 195 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 121, 159 |
| abstract_inverted_index.analysis | 50 |
| abstract_inverted_index.emission | 212 |
| abstract_inverted_index.emphasis | 4 |
| abstract_inverted_index.followed | 57 |
| abstract_inverted_index.learning | 155 |
| abstract_inverted_index.machines | 78 |
| abstract_inverted_index.methods, | 47 |
| abstract_inverted_index.networks | 66 |
| abstract_inverted_index.optimize | 82 |
| abstract_inverted_index.proposed | 103, 197 |
| abstract_inverted_index.reducing | 11, 38 |
| abstract_inverted_index.regions, | 10 |
| abstract_inverted_index.relative | 109 |
| abstract_inverted_index.research | 23, 46, 98 |
| abstract_inverted_index.warning, | 170 |
| abstract_inverted_index.accuracy. | 96 |
| abstract_inverted_index.conducted | 53 |
| abstract_inverted_index.emissions | 13, 39, 172 |
| abstract_inverted_index.important | 202 |
| abstract_inverted_index.model’s | 84 |
| abstract_inverted_index.performed | 105 |
| abstract_inverted_index.practical | 203 |
| abstract_inverted_index.reduction | 213 |
| abstract_inverted_index.Simulation | 164 |
| abstract_inverted_index.increasing | 3 |
| abstract_inverted_index.industrial | 55 |
| abstract_inverted_index.integrated | 93 |
| abstract_inverted_index.multi-task | 154 |
| abstract_inverted_index.prediction | 110, 147 |
| abstract_inverted_index.protection | 7 |
| abstract_inverted_index.short-term | 63 |
| abstract_inverted_index.application | 204 |
| abstract_inverted_index.controlling | 41 |
| abstract_inverted_index.development | 20 |
| abstract_inverted_index.effectively | 208 |
| abstract_inverted_index.electricity | 12, 181 |
| abstract_inverted_index.enterprises | 15, 36, 210 |
| abstract_inverted_index.experiments | 165 |
| abstract_inverted_index.fluctuation | 117 |
| abstract_inverted_index.prediction. | 75 |
| abstract_inverted_index.enterprises, | 56 |
| abstract_inverted_index.excellently. | 106 |
| abstract_inverted_index.introduction | 60 |
| abstract_inverted_index.environmental | 6 |
| abstract_inverted_index.requirements, | 87 |
| abstract_inverted_index.learning-based | 29, 192 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.34919754 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |