Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

Abstract

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.

Climate Change and Weather Extremes in the Eastern Mediterranean and Middle East

“…For the Arabian Peninsula, minor and insignificant changes in the mean cloudiness are projected, associated with an increase of about 5% in the cloudiness variability by mid‐century (Feron et al., 2020; IPCC, 2013). This enhanced cloudiness variability will likely influence photovoltaic energy production (Fountoulakis et al., 2021; Kosmopoulos et al., 2020). For example, summer days with very low photovoltaic power outputs will likely double in the Arabian Peninsula.…”

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

“…The longest time horizons employed in the studies span from 24 to 21 h. For example, Doorga et al [84] focused on forecasting the mesoscale distribution of surface solar irradiation within a 24 h time horizon. Kosmopoulos et al [62] aimed to forecast the impact of large-scale clouds on downwelling surface solar irradiation with a time horizon of up to 21 h. Wang et al [68] developed a method for minutely forecasting solar irradiance based on real-time sky image-irradiance mapping within a 1 h time horizon. The utilization of these diverse time horizons in solar-energy-forecasting research enables predictions at various temporal scales, facilitating decision making and resource optimization in the renewable energy sector.…”

Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms

“…Urbich et al (2018) investigated the short‐term forecasting of effective cloud albedo based on optical flow estimation methods. Kosmopoulos et al (2020) proposed using the optical flow in satellite cloud properties to forecast cloud optical thickness (COT) and cloud motion vectors, and then investigated their impact on downwelling surface solar irradiation. Nielsen et al (2020) implemented a cloud‐type nowcast from satellite data based on the optical flow method; for comparison, they also attempted several deep learning algorithms, including convolutional long‐short‐term memory (ConvLSTM) and generative adversarial networks.…”