Quantifying the Influences on Probabilistic Wind Power Forecasts

In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.Comment: 5 pages; 1 table; 3 figures; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on 667 wind and photovoltaic parks. To the best of our knowledge, this makes it the most extensive study for transfer learning in renewable power forecasts reducing the computational effort and improving the forecast error. Therefore, we adopt source models based on target data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach

Comparative study of the growth of sputtered aluminum oxide films on organic and inorganic substrates

We present a comparative study of the growth of the technologically highly relevant gate dielectric and encapsulation material aluminum oxide in inorganic and also organic heterostructures. Atomic force microscopy studies indicate strong similarities in the surface morphology of aluminum oxide films grown on these chemically different substrates. In addition, from X-ray reflectivity measurements we extract the roughness exponent \beta of aluminum oxide growth on both substrates. By renormalising the aluminum oxide roughness by the roughness of the underlying organic film we find good agreement with \beta as obtained from the aluminum oxide on silicon oxide (\beta = 0.38 \pm 0.02), suggesting a remarkable similarity of the aluminum oxide growth on the two substrates under the conditions employed