Cite
Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants.
MLA
Park, Taeseop, et al. “Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants.” Energies (19961073), vol. 16, no. 14, July 2023, p. 5293. EBSCOhost, https://doi.org/10.3390/en16145293.
APA
Park, T., Song, K., Jeong, J., & Kim, H. (2023). Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants. Energies (19961073), 16(14), 5293. https://doi.org/10.3390/en16145293
Chicago
Park, Taeseop, Keunju Song, Jaeik Jeong, and Hongseok Kim. 2023. “Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants.” Energies (19961073) 16 (14): 5293. doi:10.3390/en16145293.