A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting
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- Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
- Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
- Li, Guozhu & Ding, Chenjun & Zhao, Naini & Wei, Jiaxing & Guo, Yang & Meng, Chong & Huang, Kailiang & Zhu, Rongxin, 2024. "Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network," Energy, Elsevier, vol. 293(C).
- Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
- Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
- Chibuzor N. Obiora & Ali N. Hasan & Ahmed Ali, 2023. "Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
- Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
- Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
- Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
- Alonso-Montesinos, J. & Batlles, F.J., 2015. "Solar radiation forecasting in the short- and medium-term under all sky conditions," Energy, Elsevier, vol. 83(C), pages 387-393.
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- Guodong Wu & Diangang Hu & Yongrui Zhang & Guangqing Bao & Ting He, 2024. "A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms," Energies, MDPI, vol. 17(22), pages 1-19, November.
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Keywords
photovoltaic power forecasting; dual input; deep learning; attention mechanism; convolutional neural network;All these keywords.
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