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A Data-Driven Method for Constructing Planning Evaluation Indicators for Emerging Distribution Networks.

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Title: A Data-Driven Method for Constructing Planning Evaluation Indicators for Emerging Distribution Networks.
Authors: Zhang, Yuan1 (AUTHOR), Xiong, Wei1,2 (AUTHOR) wxiong@gzu.edu.cn, Liu, Jinsen1,2 (AUTHOR), Yuan, Xufeng1,2 (AUTHOR), Lu, Zhiyang1 (AUTHOR), Zheng, Fei2 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2310. 20p.
Subject Terms: *Power distribution networks, *Feature selection, *Empirical research, *Random forest algorithms, *Game theory, Planning techniques
Abstract: Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141425
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Data-Driven Method for Constructing Planning Evaluation Indicators for Emerging Distribution Networks.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yuan%22">Zhang, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiong%2C+Wei%22">Xiong, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wxiong@gzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jinsen%22">Liu, Jinsen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Xufeng%22">Yuan, Xufeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Zhiyang%22">Lu, Zhiyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Fei%22">Zheng, Fei</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2310. 20p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Power+distribution+networks%22">Power distribution networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br />*<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Game+theory%22">Game theory</searchLink><br /><searchLink fieldCode="DE" term="%22Planning+techniques%22">Planning techniques</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19102310
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        Text: English
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      – SubjectFull: Feature selection
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      – SubjectFull: Empirical research
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      – SubjectFull: Random forest algorithms
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      – SubjectFull: Planning techniques
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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