Kiran Teeparthi

779 total citations
46 papers, 547 citations indexed

About

Kiran Teeparthi is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Kiran Teeparthi has authored 46 papers receiving a total of 547 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Electrical and Electronic Engineering, 19 papers in Control and Systems Engineering and 11 papers in Artificial Intelligence. Recurrent topics in Kiran Teeparthi's work include Energy Load and Power Forecasting (25 papers), Power System Optimization and Stability (17 papers) and Electric Power System Optimization (15 papers). Kiran Teeparthi is often cited by papers focused on Energy Load and Power Forecasting (25 papers), Power System Optimization and Stability (17 papers) and Electric Power System Optimization (15 papers). Kiran Teeparthi collaborates with scholars based in India and United States. Kiran Teeparthi's co-authors include D. M. Vinod Kumar, Santhosh Madasthu, Santosh Kumar and Venkataramana Veeramsetty and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and Expert Systems with Applications.

In The Last Decade

Kiran Teeparthi

43 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kiran Teeparthi India 14 435 145 116 66 42 46 547
Chintham Venkaiah India 9 453 1.0× 150 1.0× 121 1.0× 112 1.7× 39 0.9× 25 530
Xiangang Peng China 12 485 1.1× 176 1.2× 117 1.0× 79 1.2× 41 1.0× 33 585
Gholamreza Memarzadeh Iran 9 500 1.1× 158 1.1× 104 0.9× 56 0.8× 46 1.1× 15 568
Zexian Sun China 7 304 0.7× 123 0.8× 78 0.7× 44 0.7× 36 0.9× 8 374
Yixiao Yu China 13 583 1.3× 271 1.9× 101 0.9× 60 0.9× 86 2.0× 36 744
Yusen Wang China 11 279 0.6× 109 0.8× 93 0.8× 45 0.7× 21 0.5× 27 565
Yingzhong Gu United States 14 707 1.6× 108 0.7× 191 1.6× 90 1.4× 41 1.0× 31 800
J.-C. Peng China 6 435 1.0× 151 1.0× 50 0.4× 67 1.0× 29 0.7× 8 561
Ümmühan Başaran Filik Türkiye 11 437 1.0× 169 1.2× 142 1.2× 49 0.7× 19 0.5× 41 575
Santhosh Madasthu India 10 418 1.0× 172 1.2× 72 0.6× 142 2.2× 36 0.9× 19 510

Countries citing papers authored by Kiran Teeparthi

Since Specialization
Citations

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

Fields of papers citing papers by Kiran Teeparthi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kiran Teeparthi

This figure shows the co-authorship network connecting the top 25 collaborators of Kiran Teeparthi. A scholar is included among the top collaborators of Kiran Teeparthi 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 Kiran Teeparthi. Kiran Teeparthi 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.
Teeparthi, Kiran, et al.. (2025). Topology and FDIA identification in distribution system state estimation using a data-driven approach. Measurement. 253. 117741–117741. 1 indexed citations
2.
Teeparthi, Kiran, et al.. (2024). A hybrid wind speed prediction model using improved CEEMDAN and Autoformer model with auto-correlation mechanism. Sustainable Energy Technologies and Assessments. 64. 103687–103687. 16 indexed citations
3.
Teeparthi, Kiran, et al.. (2024). SVMD-TF-QS: An efficient and novel hybrid methodology for the wind speed prediction. Expert Systems with Applications. 249. 123516–123516. 13 indexed citations
4.
Teeparthi, Kiran, et al.. (2024). Distribution system state estimation using physics-guided deep learning approach. Electric Power Systems Research. 236. 110922–110922. 1 indexed citations
5.
Teeparthi, Kiran, et al.. (2024). ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction. Environmental Science and Pollution Research. 31(23). 34056–34081. 5 indexed citations
6.
Teeparthi, Kiran, et al.. (2023). A hybrid VMD based contextual feature representation approach for wind speed forecasting. Renewable Energy. 219. 119391–119391. 23 indexed citations
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8.
Teeparthi, Kiran, et al.. (2023). A novel method for predicting wind speed using data decomposition-based reformer model. Earth Science Informatics. 17(1). 227–249. 7 indexed citations
9.
Teeparthi, Kiran, et al.. (2023). A review on distribution system state estimation uncertainty issues using deep learning approaches. Renewable and Sustainable Energy Reviews. 187. 113752–113752. 7 indexed citations
10.
Teeparthi, Kiran, et al.. (2023). Load Frequency Control of an Interconnected Microgrid Using MGO-based 2DOF-PID Controller. 1–6. 1 indexed citations
11.
Teeparthi, Kiran, et al.. (2023). VMD-SCINet: a hybrid model for improved wind speed forecasting. Earth Science Informatics. 17(1). 329–350. 16 indexed citations
12.
Teeparthi, Kiran, et al.. (2023). Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction. Environmental Science and Pollution Research. 30(14). 40018–40030. 9 indexed citations
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14.
Teeparthi, Kiran, et al.. (2022). Physics-guided Deep Learning for Branch Current Distribution System State Estimation. abs 1510 3820. 201–206. 1 indexed citations
15.
Teeparthi, Kiran, et al.. (2022). A novel deep learning architecture for distribution system topology identification with missing PMU measurements. Results in Engineering. 15. 100543–100543. 10 indexed citations
16.
Teeparthi, Kiran, et al.. (2022). Hybrid wind speed prediction framework using data pre-processing strategy based autoencoder network. Electric Power Systems Research. 206. 107821–107821. 22 indexed citations
17.
Madasthu, Santhosh, et al.. (2021). A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network. International Transactions on Electrical Energy Systems. 31(11). 15 indexed citations
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20.
Teeparthi, Kiran, et al.. (2021). A novel reinforced online model selection using Q-learning technique for wind speed prediction. Sustainable Energy Technologies and Assessments. 49. 101780–101780. 22 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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