Modeling of frequency containment reserve prices with econometrics and artificial intelligence

The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one-step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.     

Landscape of transcription in human cells

Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell's regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene.     

A population-specific HTR2B stop codon predisposes to severe impulsivity

Impulsivity, describing action without foresight, is an important feature of several psychiatric diseases, suicidality and violent behaviour. The complex origins of impulsivity hinder identification of the genes influencing it and the diseases with which it is associated. Here we perform exon-focused sequencing of impulsive individuals in a founder population, targeting fourteen genes belonging to the serotonin and dopamine domain. A stop codon in HTR2B was identified that is common (minor allele frequency >?1%) but exclusive to Finnish people. Expression of the gene in the human brain was assessed, as well as the molecular functionality of the stop codon, which was associated with psychiatric diseases marked by impulsivity in both population and family-based analyses. Knockout of Htr2b increased impulsive behaviours in mice, indicative of predictive validity. Our study shows the potential for identifying and tracing effects of rare alleles in complex behavioural phenotypes using founder populations, and indicates a role for HTR2B in impulsivity.     

One-day prediction of electricity load reflecting future RCS schedule

In a West-Bohemian power distribution company, a ripple control system (RCS) is used as a key tool for controlling peaks of electricity load. The RCS enables the dispatcher to switch off some groups of consumers for short periods of time, or to move their loading period in order to flatten the load diagram. Since the RCS connected consumers form about 20% of the whole load, shifts in the schedule have to be reflected in load predictions. Therefore, the load of RCS-connected consumers has been modelled and isolated from the baseload. Several similar categories of the consumers have been considered in the West Bohemian area, particularly direct heating, storage heating and boilers. Each category has been characterized by a few constants and parameters, namely installed load (a known parameter), the slope of charging and uncharging, and a daily profile of group's load. Thus, a piecewise linear model with jumps was obtained for the RCS part of the load. The baseload was modelled by means of regression on a suitable class of functions. The parameters have been estimated and utilized for a one-day-ahead prediction. This prediction reflects the scheduled times of RCS impulses, and enables dispatchers to influence the predicted diagram by changing this schedule.     

Hydrogenation and cleavage of dinitrogen to ammonia with a zirconium complex

Molecular nitrogen is relatively inert owing to the strength of its triple bond, nonpolarity and high ionization potential. As a result, the fixation of atmospheric nitrogen to ammonia under mild conditions has remained a challenge to chemists for more than a century. Although the Haber-Bosch process produces over 100 million tons of ammonia annually for the chemical industry and agriculture, it requires high temperature and pressure, in addition to a catalyst, to induce the combination of hydrogen (H2) and nitrogen (N2). Coordination of molecular nitrogen to transition metal complexes can activate and even rupture the strong N-N bond under mild conditions, with protonation yielding ammonia in stoichiometric and even catalytic yields. But the assembly of N-H bonds directly from H2 and N2 remains challenging: adding H2 to a metal-N2 complex results in the formation of N2 and metal-hydrogen bonds or, in the case of one zirconium complex, in formation of one N-H bond and a bridging hydride. Here we extend our work on zirconium complexes containing cyclopentadienyl ligands and show that adjustment of the ligands allows direct observation of N-H bond formation from N2 and H2. Subsequent warming of the complex cleaves the N-N bond at 45 degrees C, and continued hydrogenation at 85 degrees C results in complete fixation to ammonia.