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Higher Education Institutions (HEIs) should, on the one hand, provide theoretical and practical knowledge to students and, on the other hand, make valuable contributions to theoretical knowledge and provide new insights by means of research. However, HEIs have to face changing and increasing demands with respect to what they are expected to achieve. Education and research issues are no longer enough, what matters today is the so called “third mission”. A specific example for implementing a third mission is the cooperation between HEIs and business incubators. With this in mind, a local consortium consisting of regional HEIs, e.g. Bonn-Rhein-Sieg University of Applied Sciences, as well as public and private institutions and partners initiated and established an incubator hub for the region Bonn/Rhein-Sieg in 2016, called “Digital Hub Region Bonn”. This conference contribution reports on our experience with regards to this cooperation approach resulting from the above- mentioned case. Furthermore the pros and cons as well as some issues of this kind of cooperation will be discussed. Last but not least this paper initiates the opportunity to share and compare the experiences of other university business incubators in Africa as well as in Germany. As we will describe, the financial investment of HEIs in a joint-incubator with other public as well as private partners offers substantial benefits, such as mutual know-how transfer from HEIs to the economy and vice versa. This strengthens entrepreneurial mindsets and activities and contributes to the development and growth of the local economy. Consequently, this cooperation sometimes creates challenges at various levels, for example due to differing interests between HEIs and business partners. This conference contribution offers approaches to solve these issues and to support private public partnership in business incubation.
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.