Deep learning based short-term total cloud cover forecasting.

In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images. The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction. Specifically, a region-based Otsu's method is used to segment clouds from satellite images. A time-series dataset is generated using the TCC information extracted from each image in image sequences using a new feature extraction method. The generated time series data are subsequently used to train several LSTM variant models, i.e. LSTM, bi-directional LSTM and Convolutional Neural Network (CNN)-LSTM, for future TCC forecasting. Our approach achieves impressive average RMSE scores with multi-step forecasting, i.e. 0.0543 and 0.0823, with respect to both the first half of daytime and full daytime TCC forecasting on a given day, using the generated dataset

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.Comment: Published in MDPI Electronics Journa

A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications

Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this study introduces an image-based Sun tracking algorithm to localise the Sun in the image when it is visible and interpolate its daily trajectory from past observations. We validate the method on a set of sky images collected over a year at SIRTA's lab. Experimental results show that the proposed method provides robust smooth Sun trajectories with a mean absolute error below 1% of the image size.Comment: Accepted as a workshop paper at NeurIPS 202

What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate \textit{day-ahead} time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid

Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

A novel high-resolution method for forecasting cloud motion from all-sky images using deep learning is presented. A convolutional neural network (CNN) was created and trained with more than two years of all-sky images, recorded by a hemispheric sky imager (HSI) at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Using the haze indexpostprocessing algorithm, cloud characteristics were found, and the deformation vector of each cloud was performed and used as ground truth. The CNN training process was built to predict cloud motion up to 10 min ahead, in a sequence of HSI images, tracking clouds frame by frame. The first two simulated minutes show a strong similarity between simulated and measured cloud motion, which allows photovoltaic (PV) companies to make accurate horizon time predictions and better marketing decisions for primary and secondary control reserves. This cloud motion algorithm principally targets global irradiance predictions as an application for electrical engineering and in PV output predictions. Comparisons between the results of the predicted region of interest of a cloud by the proposed method and real cloud position show a mean Sørensen–Dice similarity coefficient (SD) of 94 ± 2.6% (mean ± standard deviation) for the first minute, outperforming the persistence model (89 ± 3.8%). As the forecast time window increased the index decreased to 44.4 ± 12.3% for the CNN and 37.8 ± 16.4% for the persistence model for 10 min ahead forecast. In addition, up to 10 min global horizontal irradiance was also derived using a feed-forward artificial neural network technique for each CNN forecasted image. Therefore, the new algorithm presented here increases the SD approximately 15% compared to the reference persistence model

The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Solar irradiance forecast from all-sky images using machine learning

The novel method presented here comprises techniques for cloud coverage percentage forecasts, cloud movement forecast and the subsequently prediction of the global horizontal irradiance (GHI) using all-sky images and Machine Learning techniques. Such models are employed to forecast GHI, which is necessary to make more accurate time series forecasts for photovoltaic systems like “island solutions” for power production or for energy exchange like in virtual power plants. All images were recorded by a hemispheric sky imager (HSI) at the Institute of Meteo rology and Climatology (IMuK) of the Leibniz University Hannover, Hannover, Germany. This thesis is composed of three parts. First, a model to forecast the total cloud cover five-minutes ahead by training an autoregressive neural network with Backpropagation. The prediction results showed a reduction of both the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by approximately 30% compared to the reference solar persistence solar model for various cloud conditions. Second, a model to predict the GHI up to one-hour ahead by training a Levenberg Marquardt Backpropagation neural network. This novel method reduced both the RMSE and the MAE of the one-hour prediction by approximately 40% under various weather conditions. Third, for the forecasting of the cloud movement up to two-minutes ahead, a high-resolution Deep Learning method using convolutional neural networks (CNN) was created. By taking real cloud shapes produced by the correction of the hazy areas considering the green signal counts pixels, predicted clouds shapes of the proposed algorithm was compared with the persistence solar model using the Sørensen-Dice similarity coefficient (SDC). The results of the proposed method have shown a mean SDC of 94 ± 2.6% (mean ± standard deviation) for the first minutes outperforming the persistence solar model with a SDC of 89 ± 3.8%. Thus, the proposed method may represent cloud shapes better than the persistence solar model. Finally, the Bonferroni's correction was performed so that the significance level of 0.05 was corrected to 0.05, and thus, the difference between the SDC of the proposed method and the persistence solar model was p = 0.001 being significantly high. The proposed methodologies may have broad application in the planning and management of PV power production allowing more accurate forecasts of the GHI minutes ahead by targeting primary and secondary energy control reserve