Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system
- DOI
- 10.1016/j.egyr.2023.05.229
- Published
- 2023-10
- Container
- Energy Reports
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.egyr.2023.05.229,
title = {Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system},
author = {Olusola Bamisile and Dongsheng Cai and Humphrey Adun and Chukwuebuka Ejiyi and Olufunso Alowolodu and Benjamin Ezurike and Qi Huang},
year = {2023},
journal = {Energy Reports},
doi = {10.1016/j.egyr.2023.05.229},
url = {https://doi.org/10.1016/j.egyr.2023.05.229}
}RIS
TY - JOUR TI - Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system AU - Olusola Bamisile AU - Dongsheng Cai AU - Humphrey Adun AU - Chukwuebuka Ejiyi AU - Olufunso Alowolodu AU - Benjamin Ezurike AU - Qi Huang PY - 2023 JO - Energy Reports DO - 10.1016/j.egyr.2023.05.229 UR - https://doi.org/10.1016/j.egyr.2023.05.229 ER -
APA
Bamisile, O., Cai, D., Adun, H., Ejiyi, C., Alowolodu, O., Ezurike, B., & Huang, Q. (2023). Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system. Energy Reports. https://doi.org/10.1016/j.egyr.2023.05.229
Source records
- crossref · retrieved 2026-09-25T14:22:25.603Z