Huiyuan Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Huiyuan Liu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Fu Shen
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China
1 shared publication - Huiyuan Nie
Yalong River Hdropower Development Company, Ltd., China
1 shared publication - Jian Song
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China
1 shared publication - Jian Wang
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China
1 shared publication - Jieshan Shan
Kunming University of Science and Technology
1 shared publication - Lei Kou
Qilu University of Technology (Shandong Academy of Sciences)
1 shared publication
Latest Publications
- Open Access
- 5 July 2024
- Published by Association for Computing Machinery (ACM)
- p. 11-17
Short-term prediction of solar irradiance based on TCB-GRU-MLP
In recent years, with the continuous growth of global new energy installed capacity, it is imperative to improve the prediction accuracy of new energy power generation. Based upon the current development trends of photovoltaic power generation and deep learning, this paper proposes a solar irradiance prediction model based on TCB-GRU-MLP. First, based on the Pearson and Spearman correlation analysis methods, the influencing factors that are strongly related to photovoltaic meteorological factors and irradiance in the study area were selected; then, the solar irradiance is predicted, and the results are displayed in the visualization interface. Experimental results show that the trained prediction model performs better than the TCN and Transformer models.
- 5 July 2024
- Published by Association for Computing Machinery (ACM)
- p. 11-17
Open AccessShort-term prediction of solar irradiance based on TCB-GRU-MLP
In recent years, with the continuous growth of global new energy installed capacity, it is imperative to improve the prediction accuracy of new energy power generation. Based upon the current development trends of photovoltaic power generation and deep learning, this paper proposes a solar irradiance prediction model based on TCB-GRU-MLP. First, based on the Pearson and Spearman correlation analysis methods, the influencing factors that are strongly related to photovoltaic meteorological factors and irradiance in the study area were selected; then, the solar irradiance is predicted, and the results are displayed in the visualization interface. Experimental results show that the trained prediction model performs better than the TCN and Transformer models.