Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland

This study utilizes the dynamic factor model of Giannone et al. (2008) in order to make now-/forecasts of GDP quarter-on-quarter growth rates in Switzerland. It also assesses the informational content of macroeconomic data releases for forecasting of the Swiss GDP. We find that the factor model offers a substantial improvement in forecast accuracy of GDP growth rates compared to a benchmark naive constant-growth model at all forecast horizons and at all data vintages. The largest forecast accuracy is achieved when GDP nowcasts for an actual quarter are made about three months ahead of the official data release. We also document that both business tendency surveys as well as stock market indices possess the largest informational content for GDP forecasting although their ranking depends on the underlying transformation of monthly indicators from which the common factors are extracted.Business tendency surveys, Forecasting, Nowcasting, Real-time data, Dynamic factor model

State-of-the-art climate predictions for energy climate services

Seasonal predictions of 10-m wind speed can be used by the wind energy sector in a number of decision making processes. Two different techniques of post-processing are applied in order to correct the unavoidable systematic errors present in all forecast systems. Besides an assessment of the impact of these corrections on the quality of the probabilistic forecast system is provided

State-of-the-art climate predictions for energy climate services

Seasonal predictions of 10-m wind speed can be used by the wind energy sector in a number of decision making processes. Two different techniques of post-processing are applied in order to correct the unavoidable systematic errors present in all forecast systems. Besides an assessment of the impact of these corrections on the quality of the probabilistic forecast system is provided

Coordinated operation of wind turbines and flywheel storage for primary frequency control support

This work assesses the participation of wind power plants in primary frequency control support. To participate in frequency control-related tasks, the wind power plants have to maintain a certain level of power reserves. In this article, the wind power plant is equipped with a flywheel-based storage system to fulfil the power reserve requirements set by the network operator. The article focuses on two main aspects: the definition of the control strategy to derate the wind turbines to provide a part of the required power reserves; and the coordinated regulation of the power reserves of the wind turbines and the flywheels while participating in primary frequency control. This coordinated regulation enables the wind power plant to maintain the net level of power reserves set by the network operator while alleviating the need of deloading the wind turbines. The performance of the proposed control schemes are shown by simulation. (C) 2015 Elsevier Ltd. All rights reserved.Preprin

Phase relations in K_xFe_{2-y}Se_2 and the structure of superconducting K_xFe_2Se_2 via high-resolution synchrotron diffraction

Superconductivity in iron selenides has experienced a rapid growth, but not without major inconsistencies in the reported properties. For alkali-intercalated iron selenides, even the structure of the superconducting phase is a subject of debate, in part because the onset of superconductivity is affected much more delicately by stoichiometry and preparation than in cuprate or pnictide superconductors. If high-quality, pure, superconducting intercalated iron selenides are ever to be made, the intertwined physics and chemistry must be explained by systematic studies of how these materials form and by and identifying the many coexisting phases. To that end, we prepared pure K_2Fe_4Se_5 powder and superconductors in the K_xFe_{2-y}Se_2 system, and examined differences in their structures by high-resolution synchrotron and single-crystal x-ray diffraction. We found four distinct phases: semiconducting K_2Fe_4Se_5, a metallic superconducting phase K_xFe_2Se_2 with x ranging from 0.38 to 0.58, an insulator KFe_{1.6}Se_2 with no vacancy ordering, and an oxidized phase K_{0.51(5)}Fe_{0.70(2)}Se that forms the PbClF structure upon exposure to moisture. We find that the vacancy-ordered phase K_2Fe_4Se_5 does not become superconducting by doping, but the distinct iron-rich minority phase K_xFe_2Se_2 precipitates from single crystals upon cooling from above the vacancy ordering temperature. This coexistence of metallic and semiconducting phases explains a broad maximum in resistivity around 100 K. Further studies to understand the solubility of excess Fe in the K_xFe_{2-y}Se_2 structure will shed light on the maximum fraction of superconducting K_xFe_2Se_2 that can be obtained by solid state synthesis.Comment: 12 pages, 16 figures, supplemental materia

Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling

This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners’ performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons

Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion. We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau Manubens for their technical support at different stages of this project.Peer ReviewedPostprint (author's final draft