Guangxi Yan

834 total citations
19 papers, 630 citations indexed

About

Guangxi Yan is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Guangxi Yan has authored 19 papers receiving a total of 630 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 6 papers in Control and Systems Engineering and 6 papers in Artificial Intelligence. Recurrent topics in Guangxi Yan's work include Energy Load and Power Forecasting (8 papers), Air Quality Monitoring and Forecasting (4 papers) and Solar Radiation and Photovoltaics (4 papers). Guangxi Yan is often cited by papers focused on Energy Load and Power Forecasting (8 papers), Air Quality Monitoring and Forecasting (4 papers) and Solar Radiation and Photovoltaics (4 papers). Guangxi Yan collaborates with scholars based in China and Germany. Guangxi Yan's co-authors include Chengqing Yu, Hui Liu, Zhu Duan, Chengming Yu, Xiwei Mi, Chao Chen, Haiping Wu, Yu Bai, Yu Zhang and Xinwei Liu and has published in prestigious journals such as IEEE Access, Energy and Information Sciences.

In The Last Decade

Guangxi Yan

19 papers receiving 618 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Guangxi Yan China	13	318	171	149	111	97	19	630
Chengqing Yu China	19	447 1.4×	295 1.7×	202 1.4×	150 1.4×	121 1.2×	43	1.0k
Hongyuan Luo China	10	462 1.5×	169 1.0×	274 1.8×	72 0.6×	158 1.6×	15	832
Chunlei Ji China	11	276 0.9×	208 1.2×	115 0.8×	87 0.8×	39 0.4×	27	566
Zongxi Qu China	10	503 1.6×	211 1.2×	131 0.9×	97 0.9×	50 0.5×	19	709
Hongmin Li China	18	601 1.9×	265 1.5×	269 1.8×	57 0.5×	159 1.6×	33	1.1k
Paulo S. G. de Mattos Neto Brazil	17	362 1.1×	261 1.5×	182 1.2×	47 0.4×	87 0.9×	58	847
João Fausto Lorenzato de Oliveira Brazil	16	317 1.0×	220 1.3×	103 0.7×	52 0.5×	39 0.4×	45	675
Jiazheng Li China	7	311 1.0×	179 1.0×	172 1.2×	34 0.3×	95 1.0×	12	971
Emilio G. Ortiz‐García Spain	15	480 1.5×	324 1.9×	246 1.7×	42 0.4×	39 0.4×	39	940
Kishore Kulat India	15	398 1.3×	119 0.7×	94 0.6×	58 0.5×	46 0.5×	85	836

Countries citing papers authored by Guangxi Yan

Since Specialization

Citations

This map shows the geographic impact of Guangxi Yan'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 Guangxi Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangxi Yan more than expected).

Fields of papers citing papers by Guangxi Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guangxi Yan. 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 Guangxi Yan. The network helps show where Guangxi Yan may publish in the future.

Co-authorship network of co-authors of Guangxi Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Guangxi Yan. A scholar is included among the top collaborators of Guangxi Yan 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 Guangxi Yan. Guangxi Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Yu, Chengqing, et al.. (2024). MRIformer: A multi-resolution interactive transformer for wind speed multi-step prediction. Information Sciences. 661. 120150–120150. 14 indexed citations

Yu, Chengqing, et al.. (2023). An ensemble convolutional reinforcement learning gate network for metro station PM2.5 forecasting. Stochastic Environmental Research and Risk Assessment. 39(10). 4195–4210. 8 indexed citations

Yu, Chengqing, Guangxi Yan, Chengming Yu, & Xiwei Mi. (2023). Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China. Applied Soft Computing. 148. 110864–110864. 32 indexed citations

Yu, Chengming, et al.. (2022). A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction. IEEE Access. 10. 45266–45279. 13 indexed citations

Mi, Xiwei, et al.. (2022). A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network. Digital Signal Processing. 129. 103643–103643. 19 indexed citations

Yan, Guangxi, Yu Bai, Chengqing Yu, & Chengming Yu. (2022). A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework. Machines. 10(9). 759–759. 7 indexed citations

Yan, Guangxi, Chen Jiang, Yu Bai, Chengqing Yu, & Chengming Yu. (2022). A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes. 10(4). 724–724. 56 indexed citations

Yu, Chengqing, Guangxi Yan, Chengming Yu, Yu Zhang, & Xiwei Mi. (2022). A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks. Energy. 263. 126034–126034. 99 indexed citations

Yan, Guangxi, et al.. (2022). A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China. Sustainability. 14(8). 4408–4408. 17 indexed citations

10.

Yan, Guangxi, et al.. (2022). A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy. Transportation Safety and Environment. 5(3). 2 indexed citations

11.

Liu, Xinwei, et al.. (2022). A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network. Digital Signal Processing. 123. 103419–103419. 42 indexed citations

12.

Yan, Guangxi, et al.. (2021). Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese. Applied Sciences. 11(23). 11313–11313. 10 indexed citations

13.

Schneider, Klaus, et al.. (2021). Efficient Implementation of Heterogeneous Dataflow Models using Synchronous IO Patterns. 82–89. 1 indexed citations

14.

Yan, Guangxi, Chengqing Yu, & Yu Bai. (2021). Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach. Machines. 9(11). 248–248. 19 indexed citations

15.

Dong, Shuqin, Chengqing Yu, Guangxi Yan, Jintian Zhu, & Hui Hu. (2021). A Novel Ensemble Reinforcement Learning Gated Recursive Network for Traffic Speed Forecasting. 55–60. 12 indexed citations

16.

Schneider, Klaus, et al.. (2021). Synthesis of Heterogeneous Dataflow Models from Synchronous Specifications. 2. 43–48. 1 indexed citations

17.

Yan, Guangxi, Chengqing Yu, & Yu Bai. (2021). A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting. Machines. 9(12). 312–312. 18 indexed citations

18.

Liu, Hui, Guangxi Yan, Zhu Duan, & Chao Chen. (2021). Intelligent modeling strategies for forecasting air quality time series: A review. Applied Soft Computing. 102. 106957–106957. 121 indexed citations

19.

Liu, Hui, Chengqing Yu, Haiping Wu, Zhu Duan, & Guangxi Yan. (2020). A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting. Energy. 202. 117794–117794. 139 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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