The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques.

This study analyses sky images captured using a ground-based fisheye camera, aiming to address the challenge of accurately segmenting clouds, which is difficult due to their fuzzy and indistinct boundaries and uneven lighting conditions. Accurate segmentation of clouds in ground based sky images are crucial for accurate solar energy forecasting. Motivated by these challenges, this paper has proposed a novel deep learning architecture called MSSEAG-UNet (Multi-Spatial Squeeze-and-Excite Attention Gated U-Net) for cloud segmentation in ground-based fisheye sky images. The proposed architecture integrates a Multi-Spatial Convolutional (MS-CNN) block and Squeeze-and-Excite (SE) blocks in the encoder path to improve multi-scale feature extraction and recalibrate feature maps, while an attention block is incorporated in the decoder path to emphasize key cloud features. The segmentation performance of the MSSEAG-UNet is compared with five benchmark models, and results show that the proposed model outperforms than all benchmarks models. Further, the segmented cloud images produced by the MSSEAG-UNet are used to calculate the cloud percentage, which is then integrated with the original sky images using a Multi-Column Convolutional model for Global Horizontal Irradiance (GHI) forecast. GHI forecast is conducted for 15-minute, 30-minute, and 60-minute ahead timesteps, with the best results achieved for the 60-minute forecast, yielding MAE, MSE, and RMSE values of 6.245%, 0.683%, and 8.265%, respectively. These results highlight the effectiveness of the proposed approach in improving both cloud segmentation accuracy and short-term solar irradiance forecasting.

Cloud cover significantly impacts the solar radiation reaching the Earth's surface, thereby influencing the efficiency and output of solar energy systems. Consequently, an accurate cloud segmentation approach is crucial for understanding fluctuations in solar irradiance in real time and future ahead. Such understanding aids in optimizing energy production and grid management. In this article, we designed a novel deep learning architecture called Residual Attention Gated-UNet (ResAG-UNet) for accurate cloud segmentation. The proposed ResAG-UNet integrates residual blocks in both the encoder and decoder paths, along with an attention mechanism in the decoder path. The inclusion of residual blocks facilitates faster gradient movement due to skip pathways across them, thereby enhancing training efficiency. Furthermore, the incorporation of an attention module in ResAG-UNet allows for the learning of attention coefficients for various pixels. This mechanism actively highlights crucial characteristics while suppressing less significant ones in the cloud image. The proposed ResAG-UNet model is assessed and compared with benchmark segmentation models using NITK and SWIMSEG sky datasets. The proposed approach yields mean IOU, precision, recall, F1 score, accuracy of (0.8616, 0.8826), (0.9761,0.9965), (0.9863,0.9764), (0.9237,0.9613), and (0.9424, 0.9651) on the NITK and SWIMSEG sky datasets, respectively.

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m⁻² and 230 kWh m⁻², respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.