Zhenhai Guo

2.3k total citations · 1 hit paper
30 papers, 2.0k citations indexed

About

Zhenhai Guo is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Zhenhai Guo has authored 30 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Electrical and Electronic Engineering, 10 papers in Artificial Intelligence and 9 papers in Management Science and Operations Research. Recurrent topics in Zhenhai Guo's work include Energy Load and Power Forecasting (24 papers), Electric Power System Optimization (8 papers) and Grey System Theory Applications (7 papers). Zhenhai Guo is often cited by papers focused on Energy Load and Power Forecasting (24 papers), Electric Power System Optimization (8 papers) and Grey System Theory Applications (7 papers). Zhenhai Guo collaborates with scholars based in China, Australia and United Kingdom. Zhenhai Guo's co-authors include Jianzhou Wang, Haiyan Lu, Jing Zhao, Weigang Zhao, Jianzhou Wang, Jie Wu, Xiao Xia, Wenyu Zhang, Wendong Yang and Pei Du and has published in prestigious journals such as Applied Energy, Energy Conversion and Management and Energy.

In The Last Decade

Zhenhai Guo

29 papers receiving 1.9k citations

Hit Papers

Multi-step forecasting for wind speed using a modified EM... 2011 2026 2016 2021 2011 100 200 300 400
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. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
    2011 451 citations Zhenhai Guo, Weigang Zhao et al. Renewable Energy profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhenhai Guo China 15 1.6k 612 488 460 318 30 2.0k
Xiwei Mi China 18 1.7k 1.1× 751 1.2× 381 0.8× 351 0.8× 349 1.1× 29 2.2k
Pei Du China 23 1.7k 1.0× 754 1.2× 277 0.6× 698 1.5× 381 1.2× 35 2.4k
Gong Li United States 9 1.3k 0.8× 426 0.7× 487 1.0× 244 0.5× 343 1.1× 22 1.7k
Zhu Duan China 24 962 0.6× 430 0.7× 213 0.4× 261 0.6× 553 1.7× 49 1.7k
Zi Lin United Kingdom 15 1.3k 0.8× 588 1.0× 414 0.8× 179 0.4× 177 0.6× 31 1.9k
Erasmo Cadenas Mexico 11 1.0k 0.6× 454 0.7× 347 0.7× 274 0.6× 245 0.8× 20 1.3k
Xinsong Niu China 23 1.2k 0.7× 520 0.8× 218 0.4× 454 1.0× 260 0.8× 27 1.6k
Jujie Wang China 22 1.3k 0.8× 482 0.8× 222 0.5× 933 2.0× 267 0.8× 81 2.2k
Jianming Hu China 14 1.2k 0.7× 446 0.7× 278 0.6× 297 0.6× 234 0.7× 30 1.4k

Countries citing papers authored by Zhenhai Guo

Since Specialization
Citations

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

Fields of papers citing papers by Zhenhai Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenhai Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Zhenhai Guo. A scholar is included among the top collaborators of Zhenhai Guo 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 Zhenhai Guo. Zhenhai Guo 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
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Zhao, Jing, et al.. (2020). A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions. Energy. 218. 119509–119509. 50 indexed citations
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Zhao, Jing, et al.. (2019). Wind resource assessment based on numerical simulations and an optimized ensemble system. Energy Conversion and Management. 201. 112164–112164. 8 indexed citations
6.
Zhang, Ye, Shiping Yang, Zhenhai Guo, Yanling Guo, & Jing Zhao. (2019). Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm. Atmospheric and Oceanic Science Letters. 12(2). 107–115. 20 indexed citations
7.
Zhang, Shenghui, Jiyang Wang, & Zhenhai Guo. (2018). Research on combined model based on multi-objective optimization and application in time series forecast. Soft Computing. 23(22). 11493–11521. 19 indexed citations
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Wang, Jianzhou, Tong Niu, Haiyan Lu, et al.. (2017). An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms. Applied Energy. 211. 492–512. 85 indexed citations
10.
Dong, Yunxuan, Jianzhou Wang, & Zhenhai Guo. (2017). Research and application of local perceptron neural network in highway rectifier for time series forecasting. Applied Soft Computing. 64. 656–673. 9 indexed citations
11.
Zhao, Jing, Yanling Guo, Xiao Xia, et al.. (2017). Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. Applied Energy. 197. 183–202. 123 indexed citations
12.
Wang, Jianzhou, Yunxuan Dong, Kequan Zhang, & Zhenhai Guo. (2017). A numerical model based on prior distribution fuzzy inference and neural networks. Renewable Energy. 112. 486–497. 7 indexed citations
13.
Zhao, Jing, Zhenhai Guo, Zhongyue Su, et al.. (2015). An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Applied Energy. 162. 808–826. 236 indexed citations
14.
Guo, Zhenhai & Xiao Xia. (2014). Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods. Abstract and Applied Analysis. 2014. 1–15. 11 indexed citations
15.
Guo, Zhenhai, Weigang Zhao, Haiyan Lu, & Jianzhou Wang. (2011). Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy. 37(1). 241–249. 451 indexed citations breakdown →
16.
Guo, Zhenhai, Jing Zhao, Wenyu Zhang, & Jianzhou Wang. (2011). A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. Energy. 36(3). 1668–1679. 131 indexed citations
17.
Zhang, Wenyu, Xin Liu, Zhenhai Guo, & Jianzhou Wang. (2010). Prediction of ozone concentration using back propagation neural network with a novel hybrid training algorithm. 2010 Sixth International Conference on Natural Computation. 122. 4176–4179. 1 indexed citations
18.
Guo, Zhenhai, Yao Dong, Jianzhou Wang, & Haiyan Lu. (2010). The Forecasting Procedure for Long‐Term Wind Speed in the Zhangye Area. Mathematical Problems in Engineering. 2010(1). 30 indexed citations
19.
Zhao, Jing, et al.. (2010). Effective Wind Power Density Prediction Based on Neural Networks. Lanzhou University Institutional Repository. 10. 1–4. 5 indexed citations
20.
Guo, Zhenhai, et al.. (2004). Treatment of LBCs in 2D simulation of convection over hills. Advances in Atmospheric Sciences. 21(4). 573–586. 1 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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