Load and Photovoltaic Power Scenario Extraction Based on Copula Theory and Deep Convolutional Embedded Clustering

Author:

Liu Yang,Cai Weicong,He Jie,Wu Zhenhuang,Li Zilu,Peng Xiangang,Deng Baixi

Publisher

Springer Nature Singapore

Reference17 articles.

1. Chen, G., Dong, Y., Liang, Z.: Analysis and thinking on the high-quality development of new energy with Chinese characteristics in energy transformation. Chin J. Electr. Eng. 40(17), 54935506 (2020)

2. Zhao, J., Wang, Y., Xie, H., et al.: Overview of energy storage applications in high permeability renewable energy access system. China Electr. Power 52(4), 167177 (2019)

3. Zhong, J., Li, M., Jiang, J., et al.: Research on the prediction error analysis method of wind/light output based on Copula theory. New Electr. Power Technol. 36(6), 39–46 (2017)

4. Feng, L., Zhang, J., Li, G., et al.: Cost reduction of a hybrid energy storage system considering correlation between wind and PV power. Protect. Control of Mod. Power Syst. 1, 1–9 (2016)

5. Bai, Y., Zhou, Y., Liu, J.: Cluster analysis of daily load curve based on deep convolutional embedding clustering. Power Grid Technol. 46(6), 2104–2113 (2022)

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