More than accuracy: end-to-end wind power forecasting that optimises the energy system

Weather forecast models are essential for sustainable energy systems. However, forecast accuracy may not be the best metric for developing forecast models. A more or less conservative forecast may be preferred over pure accuracy. For example, forecasting accurately in times of energy-deprived situations may be more important than in times of...

Split-based sequential sampling for realtime security assessment

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historical records. Generating diverse operating conditions is highly relevant for the uncertain future of emerging power systems that are completely different to historical power systems. In response, for the first...

Alternative Representations and Techniques for Accelerated Realistic Image Synthesis: Improved Sampling, Reconstruction and Modelling Methods

Realism has always been a major goal in visual content creation - from oil painting to motion pictures, from graphic arts to scientific data visualization. Computer graphics creates a virtual reality with digital representations. Trading between accuracy and speed, realistic rendering either creates photorealistic renders that follow strict...

Source deghosting of coarsely sampled common-receiver data using a convolutional neural network

It is well known that source deghosting can best be applied to common-receiver gathers, whereas receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in the common-shot domain, in particular when the...

A critical evaluation of the adequacy of the gamma model for representing raindrop size distributions

The adequacy of the gamma model to describe the variability of raindrop size distributions (DSD) is studied using observations from an optical disdrometer. Model adequacy is checked using a combination of Kolmogorov–Smirnov goodness-of-fit test and Kullback–Leibler divergence and the sensitivity of the results to the sampling resolution is...

Statistical Graph Signal Processing: Stationarity and Spectral Estimation

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many contemporary applications the information of interest resides in more irregular domains that can be conveniently represented using a graph. This chapter reviews...

BMC: Toolkit for Bayesian analysis of Computational Models using samplers

Background<br/>Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and...