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