Ultra-Short-Term Solar Irradiance Prediction Using an Integrated Framework with Novel Textural Convolution Kernel for Feature Extraction of Clouds

Solar irradiance is one of the main factors affecting photovoltaic power generation. The shielding effect of clouds on solar radiation is affected by both type and cover. Therefore, this paper proposes the use of textural features to represent the shielding effect of clouds on solar radiation, and a novel textural convolution kernel of a convolutional neural network, based on grey-level co-occurrence matrix, is presented to extract the textural features of clouds. An integrated ultra-short-term solar irradiance prediction framework is then proposed based on feature extraction network, a clear sky model, and LSTM. The textural features are extracted from satellite cloud images, and the theoretical irradiance under clear sky conditions is calculated based on an improved ASHRAE model. The LSTM is trained with the textural features of clouds, theoretical irradiance, and NWP information. A case study using data from Wuwei PV station in northwest China indicate that the features extracted from the proposed textural convolution kernel are better than common convolution kernels in reflecting the shielding effect of clouds on solar irradiance, and integrating textural features of cloud with theoretical irradiance can lead to better performance in solar irradiance prediction. Thus, this study will help to forecast the output power of PV stations.