Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. (June 2020)

• Record Type: Journal Article
• Authors: Zang, Haixiang; Cheng, Lilin; Ding, Tao; Cheung, Kwok W.; Wei, Zhinong; Sun, Guoqiang
• Is Part Of: International journal of electrical power & energy systems. Volume 118(2020)
• Publication Date: 2020-06
• Subjects: Photovoltaic power forecasting -- Meta learning -- Residual network -- Dense convolutional network; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105790
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Two novel deep convolutional networks, i.e. ResNet and DenseNet, are introduced to photovoltaic power forecasting. A meta learning strategy is proposed to enhance the reliability of extracted features and forecasting results. Historical PV power series, historical weather elements and weather type prediction are processed into 2D input features. Probabilistic forecasting models are built based on already-trained point forecasting models using transfer learning. Abstract: The outputs of photovoltaic (PV) power are random and uncertain due to the variations of meteorological elements, which may disturb the safety and stability of power system operation. Hence, precise day-ahead PV power forecasting is crucial in renewable energy utilization, as it is beneficial to power generation schedule and short-term dispatch of the PV integrated power grid. In this study, a novel day-ahead PV power forecasting approach based on deep learning is proposed and validated. Firstly, two novel deep convolutional neural networks (CNNs), i.e. residual network (ResNet) and dense convolutional network (DenseNet), are introduced as the core models of forecasting. Secondly, a new data preprocessing is proposed to construct input feature maps for the two novel CNNs, which involves historical PV power series, meteorological elements and numerical weather prediction. Thirdly, a meta learning strategy based on multi-loss-function network is proposed to train the two deep networks, which can ensure a high robustness of the extracted convolutional features. Owing to the learning strategy and unique architectures of the two novel CNNs, they are designed into relatively deep architectures with superb nonlinear representation abilities, which consist of more than ten layers. Both point and probabilistic forecasting results are provided in the case study, demonstrating the accuracy and reliability of the proposed forecasting approach.