Kuangpu Liu

E-mailkulienergy.aaudk

Publikationer pr. år

Hvis du har foretaget ændringer i Pure, vil de snart blive vist her.

Fingerprint

Fingerprint er automatisk genererede koncepter, som stammer fra personprofilernes indhold. Det opdateres løbende med nye registreringer.

1 Lignende profiler

1 Igangværende

Intelligent Multi-microgrids Energy Management Based on Multi-agent Deep Reinforcement Learning
Liu, K. & Chen, Z.
01/06/2021 → 31/05/2024
Projekter: Projekt › Ph.d.-projekt

5 Tidsskriftartikel
1 Konferenceartikel i proceeding

Ultra-Short-Term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique
Liao, W., Wang, S., Bak-Jensen, B., Pillai, J. R., Yang, Z. & Liu, K., 1 jul. 2023, I: Journal of Modern Power Systems and Clean Energy. 11, 4, s. 1100-1114 15 s.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
1 Citationer (Scopus)

1 Downloads (Pure)
Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks
Liao, W., Yang, Z., Liu, K., Zhang, B., Chen, X. & Song, R., 8 aug. 2022, (E-pub ahead of print) I: I E E E Transactions on Power Systems. s. 1-13 13 s.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
7 Citationer (Scopus)
Power Quality Disturbances Classification Using A TCN-CNN Model
Yang, Z., Liao, W., Liu, K., Chen, X. & Zhu, R., 2022, 2022 7th Asia Conference on Power and Electrical Engineering (ACPEE). Lie, T-T. & Liu, Y. (red.). IEEE, s. 2145-2149 5 s.
Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
2 Citationer (Scopus)
Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations
Liao, W., Bak-Jensen, B., Pillai, J. R., Yang, Z., Wang, Y. & Liu, K., 1 nov. 2022, I: Journal of Modern Power Systems and Clean Energy. 10, 6, s. 1563-1575 13 s.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
4 Citationer (Scopus)

10 Downloads (Pure)
Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach
Liao, W., Bak-Jensen, B., Pillai, J. R., Yang, Z. & Liu, K., okt. 2022, I: Electric Power Systems Research. 211, s. From 1 to 7 7 s., 108614.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
12 Citationer (Scopus)

31 Downloads (Pure)

Kuangpu Liu

Fingerprint

Samarbejde i de sidste fem år

Dyk ned i detaljerne

Intelligent Multi-microgrids Energy Management Based on Multi-agent Deep Reinforcement Learning

Ultra-Short-Term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks

Power Quality Disturbances Classification Using A TCN-CNN Model

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach

Kuangpu Liu

Fingerprint

Samarbejde i de sidste fem år

Projekter

Intelligent Multi-microgrids Energy Management Based on Multi-agent Deep Reinforcement Learning

Publikationer

Ultra-Short-Term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks

Power Quality Disturbances Classification Using A TCN-CNN Model

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach