A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning Article Swipe
Jintae Cho
,
Yeunggurl Yoon
,
Yongju Son
,
H. J. Kim
,
Ho-Sung Ryu
,
Gilsoo Jang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15092987
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/en15092987
The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en15092987
- https://www.mdpi.com/1996-1073/15/9/2987/pdf?version=1650876927
- OA Status
- gold
- Cited By
- 13
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224274174
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224274174Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en15092987Digital Object Identifier
- Title
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A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution PlanningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-19Full publication date if available
- Authors
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Jintae Cho, Yeunggurl Yoon, Yongju Son, H. J. Kim, Ho-Sung Ryu, Gilsoo JangList of authors in order
- Landing page
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https://doi.org/10.3390/en15092987Publisher landing page
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https://www.mdpi.com/1996-1073/15/9/2987/pdf?version=1650876927Direct 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
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https://www.mdpi.com/1996-1073/15/9/2987/pdf?version=1650876927Direct OA link when available
- Concepts
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Term (time), Computer science, Demand forecasting, Probabilistic forecasting, Wind power, Distribution (mathematics), Mathematical optimization, Engineering, Operations research, Artificial intelligence, Mathematics, Mathematical analysis, Physics, Electrical engineering, Probabilistic logic, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3, 2023: 4, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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