Tianlu Gao

1.1k total citations · 1 hit paper
39 papers, 667 citations indexed

About

Tianlu Gao is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Tianlu Gao has authored 39 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Electrical and Electronic Engineering, 10 papers in Control and Systems Engineering and 7 papers in Artificial Intelligence. Recurrent topics in Tianlu Gao's work include Energy Load and Power Forecasting (13 papers), Smart Grid Energy Management (11 papers) and Optimal Power Flow Distribution (10 papers). Tianlu Gao is often cited by papers focused on Energy Load and Power Forecasting (13 papers), Smart Grid Energy Management (11 papers) and Optimal Power Flow Distribution (10 papers). Tianlu Gao collaborates with scholars based in China, United States and Hong Kong. Tianlu Gao's co-authors include Jun Jason Zhang, Wenzhong Gao, Fei‐Yue Wang, Tao Wang, Xiao Wang, Peidong Xu, Qingpeng Zhang, Kelvin Kam‐Fai Tsoi, Lifang Li and Wei Duan and has published in prestigious journals such as Applied Energy, Sustainability and International Journal of Electrical Power & Energy Systems.

In The Last Decade

Tianlu Gao

37 papers receiving 646 citations

Hit Papers

Characterizing the Propagation of Situational Information... 2020 2026 2022 2024 2020 50 100 150 200 250
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. Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo
    2020 299 citations Lifang Li, Qingpeng Zhang et al. IEEE Transactions on Computational Social Systems profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tianlu Gao China 11 250 191 167 78 58 39 667
G. G. Md. Nawaz Ali United States 18 607 2.4× 164 0.9× 88 0.5× 113 1.4× 13 0.2× 69 1.0k
Eleonora D’Andrea Italy 11 149 0.6× 326 1.7× 137 0.8× 59 0.8× 37 0.6× 24 913
Muhammad Badruddin Khan Saudi Arabia 14 66 0.3× 302 1.6× 43 0.3× 19 0.2× 7 0.1× 44 783
Richard Millham South Africa 11 104 0.4× 193 1.0× 32 0.2× 25 0.3× 6 0.1× 87 658
Katayoun Farrahi United Kingdom 15 59 0.2× 126 0.7× 71 0.4× 9 0.1× 10 0.2× 37 816
M.A. Chyad Malaysia 9 37 0.1× 330 1.7× 128 0.8× 21 0.3× 20 0.3× 12 823
Carmela Comito Italy 19 35 0.1× 279 1.5× 155 0.9× 7 0.1× 22 0.4× 75 887
E. M. Almahdi Malaysia 9 46 0.2× 353 1.8× 127 0.8× 31 0.4× 21 0.4× 9 941
Kamal Chandra Paul United States 11 139 0.6× 48 0.3× 53 0.3× 41 0.5× 7 0.1× 25 386
Sungchang Lee South Korea 12 50 0.2× 196 1.0× 119 0.7× 16 0.2× 4 0.1× 44 519

Countries citing papers authored by Tianlu Gao

Since Specialization
Citations

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

Fields of papers citing papers by Tianlu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tianlu Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Tianlu Gao. A scholar is included among the top collaborators of Tianlu Gao 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 Tianlu Gao. Tianlu Gao 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.
Zhang, Jun Jason, et al.. (2025). Bearing Fault Diagnosis for Cross-Condition Scenarios Under Data Scarcity Based on Transformer Transfer Learning Network. Electronics. 14(3). 515–515. 5 indexed citations
2.
Zhang, Haoran, Peidong Xu, Tianlu Gao, et al.. (2025). Power system corrective control considering topology adjustment: An evolution-enhanced reinforcement learning method. International Journal of Electrical Power & Energy Systems. 170. 110879–110879. 1 indexed citations
3.
Zhang, Jun, et al.. (2025). Enhanced Resilience and Efficiency in Multi-Energy Systems via Stochastic Gradient-Driven Robust Optimization. Protection and Control of Modern Power Systems. 11(1). 141–156. 2 indexed citations
4.
Xu, Peidong, Jun Jason Zhang, Jian Xu, et al.. (2024). A generation-storage coordination dispatch strategy for power system based on causal reinforcement learning. Sustainable Energy Grids and Networks. 39. 101427–101427. 1 indexed citations
5.
Gao, Tianlu, et al.. (2024). Photovoltaic Power Generation Forecasting Based on TCN-Transformer Model. 620–626. 1 indexed citations
6.
Xu, Peidong, et al.. (2023). Wind Power Scenario Generation Based on Denoising Diffusion Probabilistic Model. 4525–4529. 5 indexed citations
7.
Zhou, Rui, et al.. (2022). CNN-BiLSTM Short-Term Wind Power Forecasting Method Based on Feature Selection. IEEE Journal of Radio Frequency Identification. 6. 922–927. 26 indexed citations
8.
Xu, Peidong, et al.. (2022). A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems. Frontiers in Energy Research. 10. 2 indexed citations
9.
Gao, Tianlu, et al.. (2022). Evolutionary Deep Reinforcement Learning for Volt-VAR Control in Distribution Network. 1–6. 3 indexed citations
10.
Chen, Siyuan, Jun Jason Zhang, Jian Xu, et al.. (2022). An adaptive active power rolling dispatch strategy for high proportion of renewable energy based on distributed deep reinforcement learning. Applied Energy. 330. 120294–120294. 15 indexed citations
11.
Jiang, Yuqi, et al.. (2022). Very short-term residential load forecasting based on deep-autoformer. Applied Energy. 328. 120120–120120. 53 indexed citations
12.
Xu, Peidong, et al.. (2022). Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State. IEEE Journal of Radio Frequency Identification. 7. 6–11. 5 indexed citations
13.
Gao, Tianlu, et al.. (2022). Residential Short Term Load Forecasting Based on Federated Learning. 1–6. 4 indexed citations
14.
Jiang, Huaiguang, et al.. (2021). Applications of Digital Twin System in a Smart City System with Multi-Energy. 58–61. 13 indexed citations
15.
16.
Li, Lifang, Qingpeng Zhang, Xiao Wang, et al.. (2020). Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo. IEEE Transactions on Computational Social Systems. 7(2). 556–562. 299 indexed citations breakdown →
17.
Zhou, Peng, et al.. (2020). Research on Mining of Transmission Grid Assets of Heterogeneous System Based on Digital Twin. 40. 3051–3056. 8 indexed citations
18.
Dai, Xiaoxiao, et al.. (2020). Machine Learning Assisted Clock Recovery for Pulse Position Modulation in Free Space Optical Communication. 29. M4A.253–M4A.253. 1 indexed citations
19.
Liu, Xiaohong, et al.. (2020). Social Implications of Cyber-Physical Systems in Electrical Load Forecasting. 582–587. 3 indexed citations
20.

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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