L.S. Moulin

709 total citations
11 papers, 567 citations indexed

About

L.S. Moulin is a scholar working on Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality and Artificial Intelligence. According to data from OpenAlex, L.S. Moulin has authored 11 papers receiving a total of 567 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 4 papers in Safety, Risk, Reliability and Quality and 4 papers in Artificial Intelligence. Recurrent topics in L.S. Moulin's work include Optimal Power Flow Distribution (5 papers), Energy Load and Power Forecasting (4 papers) and Neural Networks and Applications (4 papers). L.S. Moulin is often cited by papers focused on Optimal Power Flow Distribution (5 papers), Energy Load and Power Forecasting (4 papers) and Neural Networks and Applications (4 papers). L.S. Moulin collaborates with scholars based in Brazil, United States and Belgium. L.S. Moulin's co-authors include M.A. El-Sharkawi, Robert J. Marks, I. Kassabalidis, Claudia Sagastizábal, Michaël Poss, Carmen L.T. Borges and Alberto Berizzi and has published in prestigious journals such as IEEE Transactions on Power Systems, International Journal of Electrical Power & Energy Systems and Energy Systems.

In The Last Decade

L.S. Moulin

10 papers receiving 527 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
L.S. Moulin Brazil	7	479	230	147	96	41	11	567
D.C. Yu United States	9	502 1.0×	257 1.1×	79 0.5×	96 1.0×	73 1.8×	13	601
G. Irisarri United States	13	588 1.2×	157 0.7×	176 1.2×	62 0.6×	34 0.8×	20	629
M.-S. Chen United States	8	900 1.9×	559 2.4×	84 0.6×	131 1.4×	76 1.9×	15	1.0k
C.S. Özveren United Kingdom	9	454 0.9×	110 0.5×	73 0.5×	99 1.0×	71 1.7×	36	579
Ross Guttromson United States	15	551 1.2×	343 1.5×	69 0.5×	76 0.8×	28 0.7×	34	661
R.R. Shoults United States	16	1.0k 2.2×	422 1.8×	157 1.1×	84 0.9×	83 2.0×	34	1.1k
I. Drezga United States	6	487 1.0×	207 0.9×	51 0.3×	113 1.2×	103 2.5×	8	528
Hesen Liu United States	11	533 1.1×	334 1.5×	112 0.8×	105 1.1×	8 0.2×	20	655
K. Methaprayoon United States	10	560 1.2×	128 0.6×	53 0.4×	105 1.1×	83 2.0×	15	602
J. Liao United States	11	763 1.6×	180 0.8×	84 0.6×	142 1.5×	94 2.3×	14	814

Countries citing papers authored by L.S. Moulin

Since Specialization

Citations

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

Fields of papers citing papers by L.S. Moulin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by L.S. Moulin. 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 L.S. Moulin. The network helps show where L.S. Moulin may publish in the future.

Co-authorship network of co-authors of L.S. Moulin

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Borges, Carmen L.T., et al.. (2023). Assessment of most critical project delays on a multi-stage transmission expansion plan using particle swarm optimization. International Journal of Electrical Power & Energy Systems. 151. 109159–109159. 2 indexed citations

2.

Borges, Carmen L.T., et al.. (2018). Reliability Assessment of Transmission Reinforcements Delay in the Brazilian Expansion Planning. 1–6. 1 indexed citations

3.

Moulin, L.S., et al.. (2016). Neural Network Based Short-Term Electric Load Forecasting with Confidence Intervals. 7–12.

4.

Moulin, L.S., Michaël Poss, & Claudia Sagastizábal. (2010). Transmission expansion planning with re-design. Energy Systems. 1(2). 113–139. 31 indexed citations

5.

Moulin, L.S., et al.. (2004). Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems. IEEE Transactions on Power Systems. 19(2). 818–825. 287 indexed citations

6.

Moulin, L.S., et al.. (2003). Support vector and multilayer perceptron neural networks applied to power systems transient stability analysis with input dimensionality reduction. 1308–1313. 32 indexed citations

7.

Moulin, L.S., et al.. (2003). Artificial neural network-based short-term demand forecaster.. 1 indexed citations

8.

Moulin, L.S., et al.. (2002). NeuroDem-a neural network based short term demand forecaster. vol.2. 6–6. 25 indexed citations

9.

Kassabalidis, I., et al.. (2002). Dynamic security border identification using enhanced particle swarm optimization. IEEE Transactions on Power Systems. 17(3). 723–729. 83 indexed citations

10.

Marks, Robert J., et al.. (2001). Neural networks and support vector machines applied to power systems transient stability analysis. Dialnet (Universidad de la Rioja). 9(4). 205–212. 21 indexed citations

11.

Moulin, L.S., et al.. (2000). Confidence intervals for neural network based short-term load forecasting. IEEE Transactions on Power Systems. 15(4). 1191–1196. 84 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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