GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations
- DOI
- 10.1016/j.apenergy.2024.123194
- Published
- 2024-06
- Container
- Applied Energy
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.apenergy.2024.123194,
title = {GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations},
author = {Nanyang Zhu and Ying Wang and Kun Yuan and Jiahao Yan and Yaping Li and Kaifeng Zhang},
year = {2024},
journal = {Applied Energy},
doi = {10.1016/j.apenergy.2024.123194},
url = {https://doi.org/10.1016/j.apenergy.2024.123194}
}RIS
TY - JOUR TI - GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations AU - Nanyang Zhu AU - Ying Wang AU - Kun Yuan AU - Jiahao Yan AU - Yaping Li AU - Kaifeng Zhang PY - 2024 JO - Applied Energy DO - 10.1016/j.apenergy.2024.123194 UR - https://doi.org/10.1016/j.apenergy.2024.123194 ER -
APA
Zhu, N., Wang, Y., Yuan, K., Yan, J., Li, Y., & Zhang, K. (2024). GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations. Applied Energy. https://doi.org/10.1016/j.apenergy.2024.123194
Source records
- crossref · retrieved 2026-09-25T22:10:57.288Z