Cite
Day‐ahead scheduling of a hybrid renewable energy system based on generation forecasting using a deep‐learning approach.
MLA
Zamanidou, Afroditi, et al. “Day‐ahead Scheduling of a Hybrid Renewable Energy System Based on Generation Forecasting Using a Deep‐learning Approach.” Energy Science & Engineering, vol. 11, no. 5, May 2023, pp. 1688–704. EBSCOhost, https://doi.org/10.1002/ese3.1413.
APA
Zamanidou, A., Giannakopoulos, D., Pappa, D., Pitsilis, V., Makropoulos, C., & Manolitsis, K. (2023). Day‐ahead scheduling of a hybrid renewable energy system based on generation forecasting using a deep‐learning approach. Energy Science & Engineering, 11(5), 1688–1704. https://doi.org/10.1002/ese3.1413
Chicago
Zamanidou, Afroditi, Dionysios Giannakopoulos, Dimitra Pappa, Vasilis Pitsilis, Constantinos Makropoulos, and Konstantinos Manolitsis. 2023. “Day‐ahead Scheduling of a Hybrid Renewable Energy System Based on Generation Forecasting Using a Deep‐learning Approach.” Energy Science & Engineering 11 (5): 1688–1704. doi:10.1002/ese3.1413.