Heng Shi
- Electrical and Electronic Engineering top 5%
- Building and Construction top 5%
- Artificial Intelligence top 10%
- Management Science and Operations Research top 5%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Ran LiMinghao XuFurong LiZhaoyu WangKang MaChi ZhangSasa DjokicIgnacio Hernando‐Gil
- Topics
- Smart Grid Energy Management (6 papers)Energy Load and Power Forecasting (5 papers)Advanced Neural Network Applications (3 papers)
- Cited by
- Management Science and Operations ResearchBuilding and ConstructionElectrical and Electronic Engineering
- Partner nations
- United KingdomChinaFinland
In The Last Decade
Heng Shi
12 papers receiving 930 citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Electrical and Electronic Engineering 835
- Building and Construction 201
- Artificial Intelligence 197
- Management Science and Operations Research 186
- Computer Vision and Pattern Recognition 162
Countries citing papers authored by Heng Shi
This map shows the geographic impact of Heng Shi'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 Heng Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heng Shi more than expected).
Fields of papers citing papers by Heng Shi
This network shows the impact of papers produced by Heng Shi. 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 Heng Shi. The network helps show where Heng Shi may publish in the future.
Co-authorship network of co-authors of Heng Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Heng Shi. A scholar is included among the top collaborators of Heng Shi 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 Heng Shi. Heng Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 8 | |
| 3 | 5 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 45 | |
| 7 | 23 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | 1 | |
| 11 | Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNNbreakdown → | 805 |
| 12 | 27 | |
| 13 | 13 | |
| 14 | 6 | |
| 15 | 10 |
About Heng Shi
Heng Shi is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 15 papers that have together received 954 indexed citations. Recurring topics across this work include Smart Grid Energy Management (6 papers), Energy Load and Power Forecasting (5 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Management Science and Operations Research (186 citations), Building and Construction (201 citations) and Electrical and Electronic Engineering (835 citations). Heng Shi has collaborated with scholars based in United Kingdom, China and Finland. Frequent co-authors include Ran Li, Minghao Xu, Furong Li, Zhaoyu Wang, Kang Ma, Chi Zhang, Sasa Djokic, Ignacio Hernando‐Gil, Matti Lehtonen and Furong Li. Their work appears in journals such as IEEE Transactions on Power Systems, IEEE Access and IEEE Transactions on Smart Grid.
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.