Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model Article Swipe
Hao Ma
,
Peng Yang
,
Fei Wang
,
Xiaotian Wang
,
Di Yang
,
Bo Feng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16031507
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16031507
In order to effectively carry out the heavy overload monitoring and maintenance of public transformers in the distribution network, ensure the reliability of the distribution network power supply, and improve customer satisfaction with electricity consumption, this paper presents a short-term heavy overload forecasting method for public transformers based on the LSTM-XGBOOST combined model. The model extracts heavy overload feature variables from four dimensions, including basic parameter information, weather, time, and recent load, and constructs a short-term second highest load prediction model based on the LSTM algorithm to obtain the predicted value of the second highest load rate. After aggregating the heavy overload feature variables and the predicted second highest load rate, the XGboost algorithm is employed to construct a short-term heavy overload prediction model for public transformers to judge whether the public transformers display heavy overload. The test results show that this method has high accuracy in short-term heavy overload forecasting, and can effectively assist in the key monitoring and control of heavy overload in public transformers.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en16031507
- https://www.mdpi.com/1996-1073/16/3/1507/pdf?version=1675415588
- OA Status
- gold
- Cited By
- 18
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319791845
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319791845Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en16031507Digital Object Identifier
- Title
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Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-03Full publication date if available
- Authors
-
Hao Ma, Peng Yang, Fei Wang, Xiaotian Wang, Di Yang, Bo FengList of authors in order
- Landing page
-
https://doi.org/10.3390/en16031507Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/16/3/1507/pdf?version=1675415588Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1996-1073/16/3/1507/pdf?version=1675415588Direct OA link when available
- Concepts
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Transformer, Information overload, Computer science, Electricity, Reliability engineering, Engineering, Artificial intelligence, Electrical engineering, World Wide Web, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 8, 2023: 7Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.key | 157 |
| abstract_inverted_index.out | 5 |
| abstract_inverted_index.the | 6, 16, 20, 23, 49, 83, 88, 92, 99, 105, 111, 130, 156 |
| abstract_inverted_index.LSTM | 84 |
| abstract_inverted_index.four | 61 |
| abstract_inverted_index.from | 60 |
| abstract_inverted_index.high | 144 |
| abstract_inverted_index.load | 78, 95, 109 |
| abstract_inverted_index.show | 139 |
| abstract_inverted_index.test | 137 |
| abstract_inverted_index.that | 140 |
| abstract_inverted_index.this | 35, 141 |
| abstract_inverted_index.with | 32 |
| abstract_inverted_index.After | 97 |
| abstract_inverted_index.based | 47, 81 |
| abstract_inverted_index.basic | 64 |
| abstract_inverted_index.carry | 4 |
| abstract_inverted_index.heavy | 7, 40, 56, 100, 120, 134, 148, 162 |
| abstract_inverted_index.judge | 128 |
| abstract_inverted_index.load, | 71 |
| abstract_inverted_index.model | 54, 80, 123 |
| abstract_inverted_index.order | 1 |
| abstract_inverted_index.paper | 36 |
| abstract_inverted_index.power | 26 |
| abstract_inverted_index.rate, | 110 |
| abstract_inverted_index.rate. | 96 |
| abstract_inverted_index.time, | 68 |
| abstract_inverted_index.value | 90 |
| abstract_inverted_index.assist | 154 |
| abstract_inverted_index.ensure | 19 |
| abstract_inverted_index.method | 43, 142 |
| abstract_inverted_index.model. | 52 |
| abstract_inverted_index.obtain | 87 |
| abstract_inverted_index.public | 13, 45, 125, 131, 165 |
| abstract_inverted_index.recent | 70 |
| abstract_inverted_index.second | 76, 93, 107 |
| abstract_inverted_index.XGboost | 112 |
| abstract_inverted_index.control | 160 |
| abstract_inverted_index.display | 133 |
| abstract_inverted_index.feature | 58, 102 |
| abstract_inverted_index.highest | 77, 94, 108 |
| abstract_inverted_index.improve | 29 |
| abstract_inverted_index.network | 25 |
| abstract_inverted_index.results | 138 |
| abstract_inverted_index.supply, | 27 |
| abstract_inverted_index.whether | 129 |
| abstract_inverted_index.accuracy | 145 |
| abstract_inverted_index.combined | 51 |
| abstract_inverted_index.customer | 30 |
| abstract_inverted_index.employed | 115 |
| abstract_inverted_index.extracts | 55 |
| abstract_inverted_index.network, | 18 |
| abstract_inverted_index.overload | 8, 41, 57, 101, 121, 149, 163 |
| abstract_inverted_index.presents | 37 |
| abstract_inverted_index.weather, | 67 |
| abstract_inverted_index.algorithm | 85, 113 |
| abstract_inverted_index.construct | 117 |
| abstract_inverted_index.including | 63 |
| abstract_inverted_index.overload. | 135 |
| abstract_inverted_index.parameter | 65 |
| abstract_inverted_index.predicted | 89, 106 |
| abstract_inverted_index.variables | 59, 103 |
| abstract_inverted_index.constructs | 73 |
| abstract_inverted_index.monitoring | 9, 158 |
| abstract_inverted_index.prediction | 79, 122 |
| abstract_inverted_index.short-term | 39, 75, 119, 147 |
| abstract_inverted_index.aggregating | 98 |
| abstract_inverted_index.dimensions, | 62 |
| abstract_inverted_index.effectively | 3, 153 |
| abstract_inverted_index.electricity | 33 |
| abstract_inverted_index.forecasting | 42 |
| abstract_inverted_index.maintenance | 11 |
| abstract_inverted_index.reliability | 21 |
| abstract_inverted_index.LSTM-XGBOOST | 50 |
| abstract_inverted_index.consumption, | 34 |
| abstract_inverted_index.distribution | 17, 24 |
| abstract_inverted_index.forecasting, | 150 |
| abstract_inverted_index.information, | 66 |
| abstract_inverted_index.satisfaction | 31 |
| abstract_inverted_index.transformers | 14, 46, 126, 132 |
| abstract_inverted_index.transformers. | 166 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5014011433 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.90718019 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |