Binhua Dai

635 total citations
10 papers, 468 citations indexed

About

Binhua Dai is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Energy Engineering and Power Technology. According to data from OpenAlex, Binhua Dai has authored 10 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 6 papers in Artificial Intelligence and 2 papers in Energy Engineering and Power Technology. Recurrent topics in Binhua Dai's work include Energy Load and Power Forecasting (7 papers), Electric Power System Optimization (7 papers) and Solar Radiation and Photovoltaics (6 papers). Binhua Dai is often cited by papers focused on Energy Load and Power Forecasting (7 papers), Electric Power System Optimization (7 papers) and Solar Radiation and Photovoltaics (6 papers). Binhua Dai collaborates with scholars based in China. Binhua Dai's co-authors include Ming Pei, Lin Ye, Yongning Zhao, Peng Lu, Peng Lu, Yong Tang, Kaifeng Wang, Zhuo Li, Zhuo Li and Yilin Li and has published in prestigious journals such as Applied Energy, Renewable Energy and IEEE Transactions on Industry Applications.

In The Last Decade

Binhua Dai

9 papers receiving 454 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Binhua Dai China	8	398	185	75	63	44	10	468
Ming Pei China	11	473 1.2×	215 1.2×	83 1.1×	78 1.2×	49 1.1×	20	558
Shahram Hanifi United Kingdom	4	343 0.9×	177 1.0×	90 1.2×	32 0.5×	49 1.1×	6	445
Jingyi Han China	5	282 0.7×	108 0.6×	85 1.1×	48 0.8×	68 1.5×	12	385
Alper Ecemiş Türkiye	4	302 0.8×	144 0.8×	95 1.3×	29 0.5×	45 1.0×	7	367
Xuri Song China	4	328 0.8×	120 0.6×	68 0.9×	45 0.7×	31 0.7×	13	364
H. J. Lu Taiwan	8	325 0.8×	131 0.7×	70 0.9×	37 0.6×	32 0.7×	19	357
Shihao Song China	5	245 0.6×	132 0.7×	36 0.5×	46 0.7×	33 0.8×	7	317
Xiangang Peng China	12	485 1.2×	176 1.0×	79 1.1×	41 0.7×	59 1.3×	33	585
Jikai Duan China	5	247 0.6×	120 0.6×	67 0.9×	21 0.3×	50 1.1×	7	315
Zexian Sun China	7	304 0.8×	123 0.7×	44 0.6×	36 0.6×	30 0.7×	8	374

Countries citing papers authored by Binhua Dai

Since Specialization

Citations

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

Fields of papers citing papers by Binhua Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Binhua Dai

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

All Works

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10 of 10 papers shown

1.

Ye, Lin, et al.. (2023). A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment. Applied Energy. 351. 121866–121866. 11 indexed citations

2.

Ye, Lin, Binhua Dai, Ming Pei, et al.. (2022). Combined Approach for Short-Term Wind Power Forecasting Based on Wave Division and Seq2Seq Model Using Deep Learning. IEEE Transactions on Industry Applications. 58(2). 2586–2596. 50 indexed citations

3.

Ye, Lin, Binhua Dai, Zhuo Li, et al.. (2022). An ensemble method for short-term wind power prediction considering error correction strategy. Applied Energy. 322. 119475–119475. 59 indexed citations

4.

Li, Zhuo, Lin Ye, Yongning Zhao, et al.. (2022). A Spatiotemporal Directed Graph Convolution Network for Ultra-Short-Term Wind Power Prediction. IEEE Transactions on Sustainable Energy. 14(1). 39–54. 53 indexed citations

5.

Dai, Binhua, et al.. (2022). Review of Frequency Response Analysis and Evaluation Methods for New Power System. 1279–1283. 2 indexed citations

6.

Dai, Binhua, et al.. (2021). Economic Analysis of User-side Electrochemical Energy Storage Considering Time-of-Use Electricity Price. 33. 887–892. 1 indexed citations

7.

Ye, Lin, et al.. (2021). A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. Energy Reports. 8. 929–939. 56 indexed citations

8.

Lu, Peng, Lin Ye, Yongning Zhao, et al.. (2021). Feature extraction of meteorological factors for wind power prediction based on variable weight combined method. Renewable Energy. 179. 1925–1939. 39 indexed citations

9.

Lu, Peng, et al.. (2021). Short-term wind power forecasting based on meteorological feature extraction and optimization strategy. Renewable Energy. 184. 642–661. 65 indexed citations

10.

Lu, Peng, Lin Ye, Yongning Zhao, et al.. (2021). Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Applied Energy. 301. 117446–117446. 132 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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