Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight (2003.10055v1)

Published 23 Mar 2020 in physics.soc-ph, cs.SY, eess.SP, and eess.SY

Abstract: Forecasting electricity demand plays a fundamental role in the operation and planning procedures of power systems and the publications about electricity demand forecasting increasing year by year. In this paper, we use Scientometric analysis to analyze the current state and the emerging trends in the field of electricity demand forecasting form 831 publications of web of science core collection during 20 years: 1999-2018. Employing statistical description, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this article gives the most critical countries, institutions, journals, authors and publications in this field, cooperative networks relationships, research hotspots and emerging trends. The results of this article can provide meaningful guidance and some insights for researchers to find out crucial research, emerging trends and new developments in this area.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Dongchuan Yang (2 papers)
  2. Ju-e Guo (6 papers)
  3. Jie Li (553 papers)
  4. Shouyang Wang (12 papers)
  5. Shaolong Sun (7 papers)
Citations (6)

Summary

We haven't generated a summary for this paper yet.