Tie-Mao Peng

808 total citations
7 papers, 628 citations indexed

About

Tie-Mao Peng is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Tie-Mao Peng has authored 7 papers receiving a total of 628 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Electrical and Electronic Engineering, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Tie-Mao Peng's work include Energy Load and Power Forecasting (7 papers), Image and Signal Denoising Methods (3 papers) and Neural Networks and Applications (3 papers). Tie-Mao Peng is often cited by papers focused on Energy Load and Power Forecasting (7 papers), Image and Signal Denoising Methods (3 papers) and Neural Networks and Applications (3 papers). Tie-Mao Peng collaborates with scholars based in United States. Tie-Mao Peng's co-authors include G.G. Karady, N.F. Hubele, A. Papalexopoulos, Shangyou Hao and S. Hao and has published in prestigious journals such as IEEE Transactions on Power Systems.

In The Last Decade

Tie-Mao Peng

7 papers receiving 565 citations

Peers

Countries citing papers authored by Tie-Mao Peng

Since Specialization

Citations

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

Fields of papers citing papers by Tie-Mao Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tie-Mao Peng. 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 Tie-Mao Peng. The network helps show where Tie-Mao Peng may publish in the future.

Co-authorship network of co-authors of Tie-Mao Peng

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

All Works

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7 of 7 papers shown

#	Work	Indexed citations
1	Application of neural network technology to short-term system load forecasting 2005 ·A. Papalexopoulos, Shangyou Hao, Tie-Mao Peng	8
2	Conceptual approach to the application of neural network for short-term load forecasting 2002 ·Tie-Mao Peng, N.F. Hubele, G.G. Karady	13
3	Short-term system load forecasting using an artificial neural network 2002 ·A. Papalexopoulos, S. Hao, Tie-Mao Peng	11
4	AN IMPLEMENTATION OF A NEURAL NETWORK BASED LOAD FORECASTING MODEL €OR THE EMS Alex D. papalexopoulos Sfrangyou Hao 1994 ·Tie-Mao Peng	7
5	An implementation of a neural network based load forecasting model for the EMS 1994 ·IEEE Transactions on Power Systems ·A. Papalexopoulos, Shangyou Hao, Tie-Mao Peng	196
6	An adaptive neural network approach to one-week ahead load forecasting 1993 ·IEEE Transactions on Power Systems ·Tie-Mao Peng, N.F. Hubele, G.G. Karady	88
7	Advancement in the application of neural networks for short-term load forecasting 1992 ·IEEE Transactions on Power Systems ·Tie-Mao Peng, N.F. Hubele, G.G. Karady	305

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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