Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov Article Swipe
Yongbing Yan
,
Yan Shao
,
Dong Wang
,
Zhe Yang
,
Haibo Ma
,
Qingjun Li
,
Peiyi Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2024.1326525
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2024.1326525
Northeast China has been facing a severe power shortage situation. Since September 2021, “power rationing” events occurring in many places in the three provinces of northeast China have been causing inconvenience to people’s production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, and the pressure on coal and electricity will further increase. In Heilongjiang Province, due to coal capacity control, limited production led to the high price of thermal coal; wind power photovoltaic output fluctuations, the epidemic, and other reasons also resulted in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of people’s daily electricity consumption, rational distribution of power generation plans, and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of the backpropagation (BP) neural network prediction model so that the final prediction results have the advantages of the BP neural network prediction model and Markov model. In addition, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenrg.2024.1326525
- https://www.frontiersin.org/articles/10.3389/fenrg.2024.1326525/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 4
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391531877
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391531877Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fenrg.2024.1326525Digital Object Identifier
- Title
-
Prediction of the whole society electricity consumption in northeast China based on the BP neural network and MarkovWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-05Full publication date if available
- Authors
-
Yongbing Yan, Yan Shao, Dong Wang, Zhe Yang, Haibo Ma, Qingjun Li, Peiyi LiList of authors in order
- Landing page
-
https://doi.org/10.3389/fenrg.2024.1326525Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fenrg.2024.1326525/pdf?isPublishedV2=FalseDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fenrg.2024.1326525/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
-
China, Markov chain, Artificial neural network, Consumption (sociology), Electricity, Markov model, Predictive modelling, Econometrics, Environmental science, Artificial intelligence, Computer science, Economics, Engineering, Geography, Machine learning, Sociology, Electrical engineering, Social science, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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8Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6653542754, https://openalex.org/W2094743496, https://openalex.org/W4244729270, https://openalex.org/W3113689553, https://openalex.org/W1604814027, https://openalex.org/W4214893127, https://openalex.org/W2359847955, https://openalex.org/W2014542721 |
| referenced_works_count | 8 |
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