Guo‐Feng Fan

1.8k total citations · 1 hit paper
39 papers, 1.4k citations indexed

About

Guo‐Feng Fan is a scholar working on Electrical and Electronic Engineering, Management Science and Operations Research and Computer Vision and Pattern Recognition. According to data from OpenAlex, Guo‐Feng Fan has authored 39 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Electrical and Electronic Engineering, 29 papers in Management Science and Operations Research and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Guo‐Feng Fan's work include Energy Load and Power Forecasting (34 papers), Grey System Theory Applications (26 papers) and Image and Signal Denoising Methods (7 papers). Guo‐Feng Fan is often cited by papers focused on Energy Load and Power Forecasting (34 papers), Grey System Theory Applications (26 papers) and Image and Signal Denoising Methods (7 papers). Guo‐Feng Fan collaborates with scholars based in Taiwan, China and Yemen. Guo‐Feng Fan's co-authors include Wei‐Chiang Hong, Li‐Ling Peng, Fan Sun, Yi‐Hsuan Yeh, Meng Yu, Yanhui Guo, Meng Yu, Shan Qing, Yating Li and Hongjuan Li and has published in prestigious journals such as Scientific Reports, Applied Energy and Expert Systems with Applications.

In The Last Decade

Guo‐Feng Fan

35 papers receiving 1.4k citations

Hit Papers

Applications of random forest in multivariable response s... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Applications of random forest in multivariable response surface for short-term load forecasting
    2022 153 citations Guo‐Feng Fan, Wei‐Chiang Hong et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Guo‐Feng Fan Taiwan 17 916 446 304 199 183 39 1.4k
Grzegorz Dudek Poland 17 875 1.0× 378 0.8× 283 0.9× 154 0.8× 107 0.6× 59 1.2k
Xueheng Qiu Singapore 11 544 0.6× 292 0.7× 312 1.0× 131 0.7× 91 0.5× 14 973
Henrique S. Hippert Brazil 7 1.7k 1.9× 746 1.7× 505 1.7× 284 1.4× 211 1.2× 9 1.9k
Ali Fiaz United Arab Emirates 7 608 0.7× 224 0.5× 243 0.8× 99 0.5× 178 1.0× 7 972
Saifur Rahman United States 15 1.2k 1.3× 318 0.7× 301 1.0× 115 0.6× 154 0.8× 59 1.5k
Ioannis P. Panapakidis Greece 18 1.1k 1.2× 203 0.5× 217 0.7× 67 0.3× 176 1.0× 78 1.4k
Javier M. Aguiar Spain 17 1.1k 1.2× 199 0.4× 355 1.2× 215 1.1× 170 0.9× 38 1.5k
Hao Quan China 18 1.5k 1.6× 312 0.7× 662 2.2× 137 0.7× 113 0.6× 54 1.9k
J. F. Torres Spain 11 445 0.5× 234 0.5× 316 1.0× 69 0.3× 131 0.7× 22 1.1k

Countries citing papers authored by Guo‐Feng Fan

Since Specialization
Citations

This map shows the geographic impact of Guo‐Feng Fan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Guo‐Feng Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guo‐Feng Fan more than expected).

Fields of papers citing papers by Guo‐Feng Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guo‐Feng Fan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Guo‐Feng Fan. The network helps show where Guo‐Feng Fan may publish in the future.

Co-authorship network of co-authors of Guo‐Feng Fan

This figure shows the co-authorship network connecting the top 25 collaborators of Guo‐Feng Fan. A scholar is included among the top collaborators of Guo‐Feng Fan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guo‐Feng Fan. Guo‐Feng Fan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fan, Guo‐Feng, et al.. (2025). Application of ensemble empirical mode decomposition with support vector regression and wavelet neural network in electric load forecasting. Energy Sources Part B Economics Planning and Policy. 20(1). 3 indexed citations
2.
Peng, Li‐Ling, et al.. (2025). Uncertainty quantification and load forecasting based on CPO-VMD and hybrid deep learning. Electric Power Systems Research. 252. 112444–112444.
3.
Fan, Guo‐Feng, et al.. (2024). The volatility mechanism and intelligent fusion forecast of new energy stock prices. Financial Innovation. 10(1). 6 indexed citations
4.
Fan, Guo‐Feng, et al.. (2024). Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction. Renewable Energy. 234. 121174–121174. 8 indexed citations
5.
Peng, Li‐Ling, et al.. (2024). Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management. Journal of Hydro-environment Research. 56. 28–39.
6.
Fan, Guo‐Feng, et al.. (2023). A new intelligent hybrid forecasting method for power load considering uncertainty. Knowledge-Based Systems. 280. 111034–111034. 20 indexed citations
7.
Fan, Guo‐Feng, et al.. (2023). Use of weighted local constant method to short-term forecasting of electric load in cities at weekends. Electric Power Systems Research. 221. 109464–109464. 4 indexed citations
8.
Fan, Guo‐Feng, et al.. (2023). Forecasting residential electricity consumption using the novel hybrid model. Energy and Buildings. 290. 113085–113085. 19 indexed citations
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Fan, Guo‐Feng, et al.. (2021). Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City. Journal of the Operational Research Society. 73(7). 1474–1486. 5 indexed citations
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13.
Fan, Guo‐Feng, et al.. (2020). Fault detection in switching process of a substation using the SARIMA–SPC model. Scientific Reports. 10(1). 11417–11417. 5 indexed citations
14.
Fan, Guo‐Feng, Xiao Wei, Yating Li, & Wei‐Chiang Hong. (2020). Forecasting electricity consumption using a novel hybrid model. Sustainable Cities and Society. 61. 102320–102320. 77 indexed citations
15.
16.
Fan, Guo‐Feng, et al.. (2019). Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting. Energies. 12(5). 916–916. 109 indexed citations
17.
Fan, Guo‐Feng, et al.. (2019). Application of GM (1,1) and Seasonal Cross-Trend Model in the Forecast of Tourist Population in Sanya. RePEc: Research Papers in Economics. 10(4). 51–64. 2 indexed citations
18.
Hong, Wei‐Chiang, Mingwei Li, & Guo‐Feng Fan. (2019). Short-Term Load Forecasting by Artificial Intelligent Technologies. BiblioBoard Library Catalog (Open Research Library). 7 indexed citations
19.
Fan, Guo‐Feng, Li‐Ling Peng, Xiangjun Zhao, & Wei‐Chiang Hong. (2017). Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model. Energies. 10(11). 1713–1713. 50 indexed citations
20.
Fan, Guo‐Feng, Shan Qing, Hua Wang, et al.. (2012). Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time‐Series. Mathematical Problems in Engineering. 2012(1). 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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