Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators

We present a specialized scenario generation method that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power generation scenarios by sampling from a conditional distribution that uses day-ahead wind speed forecasts to tailor the scenarios to the specific day. We apply the generated scenarios in a simple stochastic day-ahead bidding problem of a wind electricity producer and run a statistical analysis focusing on whether the scenarios yield profitable and reliable decisions. Compared to conditional scenarios generated from Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow scenarios identify the daily trends more accurately and with a lower spread while maintaining a diverse variety. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more profitable and reliable results compared to an unconditional selection of historical scenarios. The obtained profits using the normalizing flow scenarios are consistently closest to the perfect foresight solution, in particular, for small sets of only five scenarios.Comment: manuscript (17 pages, 7 figures, 5 tables), supporting information (2 pages, 1 figure, 1 table

Validation Methods for Energy Time Series Scenarios From Deep Generative Models

The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented by a set of discrete realizations known as scenarios. A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. However, the validation of generated scenarios is difficult, and a comprehensive discussion about appropriate validation methods is currently lacking. To start this discussion, we provide a critical assessment of the currently used validation methods in the energy scenario generation literature. In particular, we assess validation methods based on probability density, auto-correlation, and power spectral density. Furthermore, we propose using the multifractal detrended fluctuation analysis (MFDFA) as an additional validation method for non-trivial features like peaks, bursts, and plateaus. As representative examples, we train generative adversarial networks (GANs), Wasserstein GANs (WGANs), and variational autoencoders (VAEs) on two renewable power generation time series (photovoltaic and wind from Germany in 2013 to 2015) and an intra-day electricity price time series form the European Energy Exchange in 2017 to 2019. We apply the four validation methods to both the historical and the generated data and discuss the interpretation of validation results as well as common mistakes, pitfalls, and limitations of the validation methods. Our assessment shows that no single method sufficiently characterizes a scenario but ideally validation should include multiple methods and be interpreted carefully in the context of scenarios over short time periods.Comment: 20 pages, 8 figures, 2 table

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms

Meta-learning of numerical algorithms for a given task consist of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To limit the complexity of the meta-learning problem, neural architectures with a certain inductive bias towards favorable algorithmic structures can, and should, be used. We generalize our previously introduced Runge-Kutta neural network to a recursively recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms. In contrast to off-the-shelf deep learning approaches, it features a distinct division into modules for generation of information and for the subsequent assembly of this information towards a solution. Local information in the form of a subspace is generated by subordinate, inner, iterations of recurrent function evaluations starting at the current outer iterate. The update to the next outer iterate is computed as a linear combination of these evaluations, reducing the residual in this space, and constitutes the output of the network. We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields iterations similar to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta integrators for ordinary differential equations. Due to its modularity, the superstructure can be readily extended with functionalities needed to represent more general classes of iterative algorithms traditionally based on Taylor series expansions.Comment: manuscript (21 pages, 10 figures), supporting information (2 pages, 1 figure

The Bolocam Galactic Plane Survey IV: 1.1 and 0.35 mm Dust Continuum Emission in the Galactic Center Region

The Bolocam Galactic Plane Survey (BGPS) data for a six square degree region of the Galactic plane containing the Galactic center is analyzed and compared to infrared and radio continuum data. The BGPS 1.1 mm emission consists of clumps interconnected by a network of fainter filaments surrounding cavities, a few of which are filled with diffuse near-IR emission indicating the presence of warm dust or with radio continuum characteristic of HII regions or supernova remnants. New 350 {\mu}m images of the environments of the two brightest regions, Sgr A and B, are presented. Sgr B2 is the brightest mm-emitting clump in the Central Molecular Zone and may be forming the closest analog to a super star cluster in the Galaxy. The Central Molecular Zone (CMZ) contains the highest concentration of mm and sub-mm emitting dense clumps in the Galaxy. Most 1.1 mm features at positive longitudes are seen in silhouette against the 3.6 to 24 {\mu}m background observed by the Spitzer Space Telescope. However, only a few clumps at negative longitudes are seen in absorption, confirming the hypothesis that positive longitude clumps in the CMZ tend to be on the near-side of the Galactic center, consistent with the suspected orientation of the central bar in our Galaxy. Some 1.1 mm cloud surfaces are seen in emission at 8 {\mu}m, presumably due to polycyclic aromatic hydrocarbons (PAHs). A ~0.2\degree (~30 pc) diameter cavity and infrared bubble between l \approx 0.0\degree and 0.2\degree surrounds the Arches and Quintuplet clusters and Sgr A. The bubble contains several clumpy dust filaments that point toward Sgr A\ast; its potential role in their formation is explored. [abstract truncated]Comment: 76 pages, 22 figures, published in ApJ: http://iopscience.iop.org/0004-637X/721/1/137

Clinical Characteristics of Inpatients with Childhood vs. Adolescent Anorexia Nervosa

We aimed to compare the clinical data at first presentation to inpatient treatment of children (<14 years) vs. adolescents (≥14 years) with anorexia nervosa (AN), focusing on duration of illness before hospital admission and body mass index (BMI) at admission and discharge, proven predictors of the outcomes of adolescent AN. Clinical data at first admission and at discharge in 289 inpatients with AN (children: n = 72; adolescents: n = 217) from a German multicenter, web-based registry for consecutively enrolled patients with childhood and adolescent AN were analyzed. Inclusion criteria were a maximum age of 18 years, first inpatient treatment due to AN, and a BMI <10th BMI percentile at admission. Compared to adolescents, children with AN had a shorter duration of illness before admission (median: 6.0 months vs. 8.0 months, p = 0.004) and higher BMI percentiles at admission (median: 0.7 vs. 0.2, p = 0.004) as well as at discharge (median: 19.3 vs. 15.1, p = 0.011). Thus, in our study, children with AN exhibited clinical characteristics that have been associated with better outcomes, including higher admission and discharge BMI percentile. Future studies should examine whether these factors are actually associated with positive long-term outcomes in children

Auf dem Weg zur klimaneutralen Industrie - Herausforderungen und Strategien

Der Beitrag beleuchtet die Herausforderungen und Strategien für die Industrie in Deutschland im Rahmen der Energiewende. Der Fokus liegt hierbei auf den Themen Kreislaufwirtschaft, Wasserstoffnutzung, Erneuerbare Prozesswärme und Bioenergie sowie auf den sich darausergebenden Herausforderungen an die Infrastruktur und die Politik

Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts

Peer reviewe

Shared genetic risk between eating disorder- and substance-use-related phenotypes:Evidence from genome-wide association studies

First published: 16 February 202