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The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
As from the beginning of the late 70's an impressive number of innovative electronic payment systems have been developed and tested commercially. However, the resulting variety and complexity of the systems have turned out to be one of the obstacles for the broad acceptance of electronic payment. In this paper we propose a process and service oriented framework, which offers a structural and conceptual orientation in the field of electronic payment. It renders possible an integral view on electronic payment that goes beyond the frame of an individual system. To do this, we have generalized the systems-oriented approaches to a phase-oriented payment model. Using this model, requirements and supporting services for electronic payment can be sorted systematically and described from both the customers' and the merchants' viewpoint. Providing integrated payment processes and services is proving to be a difficult task. With this paper we would like to outline the necessity for a Payment Service Provider to act as a mediator for suppliers and users of electronic payment systems.