SolarNet: A sky image-based deep convolutional neural network for intra-hour solar forecasting. (1st July 2020)

• Record Type: Journal Article
• Authors: Feng, Cong; Zhang, Jie
• Is Part Of: Solar energy. Volume 204(2020)
• Page Start: 71
• Page End: 78
• Publication Date: 2020-07-01
• Subjects: Deep learning -- CNN -- Solar forecasting -- Sky image processing; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2020.03.083
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: The SolarNet generates accurate intra-hour solar forecasts from a single sky image. The SolarNet outperforms machine learning models under various weather conditions. The SolarNet has 8.85% forecasting nRMSE and a 25.14% forecasting skill score. Abstract: The exponential growth of solar energy poses challenges to power systems, mostly due to its uncertain and variable characteristics. Hence, solar forecasting, such as very short-term solar forecasting (VSTSF), has been widely adopted to assist power system operations. The VSTSF takes inputs from various sources, among which sky image-based VSTSF is not yet well-studied compared to its counterparts. In this paper, a deep convolutional neural network (CNN) model, called the SolarNet, is developed to forecast the operational 1-h-ahead global horizontal irradiance (GHI) by only using sky images without numerical measurements and extra feature engineering. The SolarNet has a set of models that generate fixed-step GHI in parallel. Each model is composed of 20 convolutional, max-pooling, and fully-connected layers, which learns latent patterns between sky images and GHI in an end-to-end manner. Numerical results based on six years data show that the developed SolarNet outperforms the benchmarking persistence of cloudiness model and machine learning models with an 8.85% normalized root mean square error and a 25.14% forecasting skill score. The SolarNet shows superiority under various weather conditions.