Intra-hour photovoltaic power point-interval prediction using a dual-view deep neural network

• Published in: Expert systems with applications Vol. 260; p. 125368
• Main Authors: Chen, Zhi-ru, Bai, Yu-long, Ding, Lin, Qin, Hao-yu, Bi, Qi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2025
• Subjects: Gate recurrent unit; Kernel density estimation; Multi-step prediction; PV power prediction; Temporal convolutional network; Multi-step prediction; Temporal convolutional network; Kernel density estimation; Gate recurrent unit; PV power prediction
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125368

•A dual-view deep neural network is proposed for short-term PV power prediction.•Different types of prediction tasks are used to evaluate the effectiveness of the proposed model extensively.•Interval prediction methods with and without parameters are compared. Photovoltaic (PV) power is intermittent, fluctuating, and uncertain due to meteorological factors, which makes it challenging to integrate PV power into electrical grids. Overcoming this problem requires an accurate and reliable PV power prediction model; thus, a dual-view deep neural network (Dv-DNN) is proposed. The model fuses a gate recurrent unit (GRU) neural network and a temporal convolutional network (TCN) in parallel, enabling end-to-end dual-view deep prediction for PV power. Using data from solar systems in the Yulara region of Australia, three sets of experiments—single-step, multi-step, and interval prediction—are conducted to explore the performance of two recurrent and convolutional deep neural networks in PV power prediction. For point prediction, the experimental results show that the Dv-DNN has better forecasting performance than other models as the prediction horizon increases. Regarding interval prediction, at confidence levels of 97.5%, 95%, 90%, and 85%, the method based on kernel density estimation (KDE) is superior to the method based on maximum likelihood estimation (MLE) according to the coverage width-based criterion (CWC).