Yuan Gao

3.1k total citations · 1 hit paper
138 papers, 2.1k citations indexed

About

Yuan Gao is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Building and Construction. According to data from OpenAlex, Yuan Gao has authored 138 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Electrical and Electronic Engineering, 31 papers in Artificial Intelligence and 22 papers in Building and Construction. Recurrent topics in Yuan Gao's work include Building Energy and Comfort Optimization (22 papers), Energy Load and Power Forecasting (17 papers) and Smart Grid Energy Management (14 papers). Yuan Gao is often cited by papers focused on Building Energy and Comfort Optimization (22 papers), Energy Load and Power Forecasting (17 papers) and Smart Grid Energy Management (14 papers). Yuan Gao collaborates with scholars based in China, Japan and United States. Yuan Gao's co-authors include Yingjun Ruan, Yasunori Akashi, Shohei Miyata, Shuai Yin, Marios Koufaris, Nadia Bianchi‐Berthouze, Hongying Meng, Zehuan Hu, Brett A. Clementz and Masayuki MAE and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of The Electrochemical Society.

In The Last Decade

Yuan Gao

121 papers receiving 2.0k citations

Hit Papers

Improved multistep ahead ... 2024 2026 2024 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuan Gao China 25 754 486 448 259 224 138 2.1k
Martin Raubal Switzerland 35 367 0.5× 442 0.9× 436 1.0× 236 0.9× 95 0.4× 171 3.6k
Jun Jo Australia 25 466 0.6× 262 0.5× 341 0.8× 66 0.3× 72 0.3× 131 2.5k
Ricardo A. Ramírez-Mendoza Mexico 28 336 0.4× 145 0.3× 388 0.9× 180 0.7× 149 0.7× 252 3.3k
Hui Fang China 31 259 0.3× 83 0.2× 457 1.0× 400 1.5× 143 0.6× 255 3.4k
Flora D. Salim Australia 27 400 0.5× 762 1.6× 541 1.2× 55 0.2× 40 0.2× 199 2.6k
Francisco J. Martínez Spain 30 2.1k 2.8× 326 0.7× 260 0.6× 90 0.3× 90 0.4× 107 3.5k
Amanda D. Smith United States 21 1.0k 1.4× 677 1.4× 165 0.4× 109 0.4× 438 2.0× 52 2.2k
Xiaofan Jiang United States 24 1.8k 2.4× 293 0.6× 189 0.4× 96 0.4× 105 0.5× 161 3.0k
Josef F. Krems Germany 42 1.9k 2.6× 150 0.3× 220 0.5× 298 1.2× 710 3.2× 186 6.1k
Eric Paulos United States 39 702 0.9× 167 0.3× 134 0.3× 462 1.8× 21 0.1× 130 4.5k

Countries citing papers authored by Yuan Gao

Since Specialization
Citations

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

Fields of papers citing papers by Yuan Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuan Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan Gao. A scholar is included among the top collaborators of Yuan 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 Yuan Gao. Yuan 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
2.
Zhou, Qi, Mingzhe Liu, Zhe Wang, Yangyang Fu, & Yuan Gao. (2025). Accelerating Reinforcement Learning controller training for building energy management: A hybrid co-simulation approach. Science and Technology for the Built Environment. 31(5). 515–532.
3.
Gao, Yuan, Mingzhe Liu, Zehuan Hu, et al.. (2025). Quantitative analysis of energy justice in demand response: Insights from real residential data in Texas, USA. Renewable Energy. 242. 122477–122477. 2 indexed citations
4.
Liu, Mingzhe, Wei-An Chen, Yuan Gao, & Zehuan Hu. (2025). Comparative Analysis of Battery and Thermal Energy Storage for Residential Photovoltaic Heat Pump Systems in Building Electrification. Sustainability. 17(21). 9497–9497.
5.
Yan, Ke, et al.. (2025). A stable, reliable and interpretable diffusion model for HVAC FDD with data unavailability. Applied Energy. 401. 126774–126774.
6.
Hu, Zehuan, Yuan Gao, Luning Sun, & Masayuki MAE. (2025). A novel attention-enhanced LLM approach for accurate power demand and generation forecasting. Renewable Energy. 252. 123465–123465. 4 indexed citations
7.
Gao, Yuan, et al.. (2024). Engineering application and stability verification of non-premixed UHPC production technology. Case Studies in Construction Materials. 21. e03724–e03724. 1 indexed citations
8.
Gao, Yuan, et al.. (2024). Render help or stand by? The effect of group size on third-party punishment and its neural mechanisms. Behavioural Brain Research. 476. 115256–115256. 1 indexed citations
9.
Gao, Yuan, et al.. (2024). RESEARCH ON THE IMPACT OF ADVERTISING IMPLANTATION CHARACTERISTICS ON USER PURCHASE INTENTION ON SELF MEDIA PLATFORMS. The EUrASEANs journal on global socio-economic dynamics. 7–25.
10.
Gao, Yuan, et al.. (2024). Emotional contextual effects of face perception: a test of the affective realism hypothesis. The Journal of General Psychology. 152(2). 237–264.
11.
Gao, Yuan, Zehuan Hu, Wei-An Chen, Mingzhe Liu, & Yingjun Ruan. (2024). A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting. Applied Energy. 378. 124844–124844. 12 indexed citations
12.
Liu, Mingzhe, Mingyue Guo, Yangyang Fu, Zheng O’Neill, & Yuan Gao. (2024). Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications. Applied Energy. 372. 123753–123753. 15 indexed citations
13.
Gao, Yuan, Zehuan Hu, Wei-An Chen, & Mingzhe Liu. (2024). Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning. Energy. 302. 131863–131863. 5 indexed citations
14.
Gao, Yuan, Shohei Miyata, & Yasunori Akashi. (2023). How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method. Applied Energy. 348. 121591–121591. 38 indexed citations
15.
Wang, Xueying, et al.. (2023). A Novel Ultra-short-term Photovoltaic Power Generation Forecasting Method Based on Seasonal Autoregressive Integrated Moving Average. Journal of Physics Conference Series. 2427(1). 12006–12006. 2 indexed citations
16.
17.
Tan, Xiao, Zhigang Wang, Jian Wang, et al.. (2019). Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features. Computer Vision and Pattern Recognition. 275–284. 23 indexed citations
18.
Gao, Yuan, et al.. (2018). An Empirical Study on the Effectiveness of Gesture in L2 Listening Class. 40(4). 115–130. 1 indexed citations
19.
McDowell, Jennifer E., Johanna Kißler, Patrick Berg, et al.. (2005). Electroencephalography/magnetoencephalography study of cortical activities preceding prosaccades and antisaccades. Neuroreport. 16(7). 663–668. 63 indexed citations
20.
Gao, Yuan. (2004). Applying the Technology Acceptance Model to Educational Hypermedia: A Field Study. Journal of educational multimedia and hypermedia. 14(1). 237–247. 65 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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