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

Inhaler devices in asthma and COPD patients : a prospective cross-sectional study on inhaler preferences and error rates

Background: Inhalation therapy is the backbone of asthma and COPD control. However, inhaler adherence and device mishandling continue to be a problem in real life. Some studies have shown that using a patient-preferred inhaler may reduce device handling errors and improve adherence to prescribed chronic inhaler drug therapy. The aim of this study was to compare the preferences for commonly used inhaler devices in Germany in patients with chronic obstructive respiratory disease. We also pursued the question which properties of an inhaler device are particularly important to the user and what effects age, gender and type of disease (asthma or COPD) may have on device preference and handling errors. Methods: Prospective, open-label cross-sectional study in which 105 patients with asthma (58%) or COPD (42%) participated. Validated checklists were used to objectively assess inhaler technique and errors with 10 different placebo devices. For each device, patients were asked to test the handling, to assess the device properties and to name the device that they would most or least prefer. Results: Across the 10 placebo inhaler devices tested, patients needed an average of 1.22 attempts to error-free use. The device with the lowest mean number of attempts was the Turbohaler® (1.02), followed by the Nexthaler® (1.04), the Diskus® (1.07) and the Spiromax® (1.10). Patients over 60 years vs. younger age (p = 0.002) and COPD vs. asthma patients (p = 0.016) required more attempts to ensure correct use. 41% of the study participants chose one of the devices they already used as the most preferred inhaler. Overall, 20% opted for the Spiromax®, 15% for the Nexthaler® and 14% for the Turbohaler® or a pMDI. The least preferred device was the Elpenhaler® (0%). From a selection of 7 predefined inhaler attributes, patients stated easy handling as the most important for them. This was followed by short inhalation time and low inhalation resistance. Conclusions: Patient preference may vary between inhaler devices. The lowest number of attempts to error-free use was reported for the Turbohaler® and the Nexthaler®. The Spiromax® and the Nexthaler® achieved the best overall ratings and were the devices most preferred by patients.DFG-Publikationsfonds 202