Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid Article Swipe
Wenhao Chen
,
Guangjie Han
,
Hongbo Zhu
,
Lyuchao Liao
,
Wenqing Zhao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142416894
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/su142416894
Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su142416894
- https://www.mdpi.com/2071-1050/14/24/16894/pdf?version=1671412624
- OA Status
- gold
- Cited By
- 11
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311618821
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311618821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su142416894Digital Object Identifier
- Title
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Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart GridWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-16Full publication date if available
- Authors
-
Wenhao Chen, Guangjie Han, Hongbo Zhu, Lyuchao Liao, Wenqing ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/su142416894Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/14/24/16894/pdf?version=1671412624Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/14/24/16894/pdf?version=1671412624Direct OA link when available
- Concepts
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Residual, Computer science, Snapshot (computer storage), Smart grid, Grid, Ensemble forecasting, Data mining, Network model, Volatility (finance), Artificial intelligence, Algorithm, Database, Engineering, Economics, Financial economics, Electrical engineering, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 4, 2023: 3Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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