Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight
Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.771433/full |
| _version_ | 1819124898036973568 |
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| author | Dongchuan Yang Ju-e Guo Jie Li Jie Li Shouyang Wang Shouyang Wang Shouyang Wang Shaolong Sun |
| author_facet | Dongchuan Yang Ju-e Guo Jie Li Jie Li Shouyang Wang Shouyang Wang Shouyang Wang Shaolong Sun |
| author_sort | Dongchuan Yang |
| collection | DOAJ |
| description | Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999–2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting. |
| first_indexed | 2024-12-22T07:31:33Z |
| format | Article |
| id | doaj.art-448d543051b646c79b188b43b8e4d45b |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-22T07:31:33Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-448d543051b646c79b188b43b8e4d45b2022-12-21T18:34:00ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-10-01910.3389/fenrg.2021.771433771433Knowledge Mapping in Electricity Demand Forecasting: A Scientometric InsightDongchuan Yang0Ju-e Guo1Jie Li2Jie Li3Shouyang Wang4Shouyang Wang5Shouyang Wang6Shaolong Sun7School of Management, Xi’an Jiaotong University, Xi’an, ChinaSchool of Management, Xi’an Jiaotong University, Xi’an, ChinaNational Science Library, Chinese Academy of Sciences, Beijing, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Fuxin, ChinaAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaSchool of Economics and Management, University of Chinese Academy of Sciences, Beijing, ChinaCenter for Forecasting Science, Chinese Academy of Sciences, Beijing, ChinaSchool of Management, Xi’an Jiaotong University, Xi’an, ChinaElectricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999–2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting.https://www.frontiersin.org/articles/10.3389/fenrg.2021.771433/fullelectricity demand forecastingscientometricvisualizationcitespaceknowledge mapping |
| spellingShingle | Dongchuan Yang Ju-e Guo Jie Li Jie Li Shouyang Wang Shouyang Wang Shouyang Wang Shaolong Sun Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight Frontiers in Energy Research electricity demand forecasting scientometric visualization citespace knowledge mapping |
| title | Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight |
| title_full | Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight |
| title_fullStr | Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight |
| title_full_unstemmed | Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight |
| title_short | Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight |
| title_sort | knowledge mapping in electricity demand forecasting a scientometric insight |
| topic | electricity demand forecasting scientometric visualization citespace knowledge mapping |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.771433/full |
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