APA (7th ed.) Citation

Liu, Z., Liu, Y., Chen, X., Zhang, S., Luo, X., Li, L., . . . You, G. (2024). A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting. Applied Energy, 360, N.PAG. https://doi.org/10.1016/j.apenergy.2024.122785

Chicago Style (17th ed.) Citation

Liu, Zhi-Feng, You-Yuan Liu, Xiao-Rui Chen, Shu-Rui Zhang, Xing-Fu Luo, Ling-Ling Li, Yi-Zhou Yang, and Guo-Dong You. "A Novel Deep Learning-based Evolutionary Model with Potential Attention and Memory Decay-enhancement Strategy for Short-term Wind Power Point-interval Forecasting." Applied Energy 360 (2024): N.PAG. https://doi.org/10.1016/j.apenergy.2024.122785.

MLA (9th ed.) Citation

Liu, Zhi-Feng, et al. "A Novel Deep Learning-based Evolutionary Model with Potential Attention and Memory Decay-enhancement Strategy for Short-term Wind Power Point-interval Forecasting." Applied Energy, vol. 360, 2024, p. N.PAG, https://doi.org/10.1016/j.apenergy.2024.122785.