The influence of weather regimes on European renewable energy production and demand

The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime -mean and extreme- wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the Scandinavian Blocking and NAO negative regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 2.0 and 1.5, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-)seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential

Study of excited nucleon states at EBAC: status and plans

We present an overview of a research program for the excited nucleon states in Excited Baryon Analysis Center (EBAC) at Jefferson Lab. Current status of our analysis of the meson production reactions based on the unitary dynamical coupled-channels model is summarized, and the N* pole positions extracted from the constructed scattering amplitudes are presented. Our plans for future developments are also discussed.Comment: Plenary talk given at Workshop on the Physics of Excited Nucleon -- NSTAR2009, Beijing, April 19-22, 2009. 8 pages, 8 figure

Doing data analysis

'Research is about more than empirical evidence, but evidence is at the heart of finding out more about the social and education world. One way of marshalling evidence on a topic, or to answer a research question, is to use the findings of others as published in the literature. This use of evidence at third-hand is common – in the notorious literature review for a PhD, for example. I say ‘third-hand’ because the analyst does not have access to the primary evidence, nor are they re-presenting an analysis of the data. They are presenting a summary of what a previous author presented about an analysis of data. Done well, with a clear focus, such a review of literature can be useful, at least in establishing what others think, how a topic is usually researched, and why the topic might be important to research further. Some of the inherent weaknesses of using the accounts of others might be overcome by ensuring that all of the relevant literature was used, even accounts of unsuccessful studies and evidence from unpublished studies, and then conducting a full meta-analysis of the results (I recommend using a Bayesian approach, see appendix to Gorard et al. 2004, which allows the relatively simple combination of different kinds of evidence). But such systematic reviews of evidence are rare, very difficult to do properly, and both expensive and time-consuming. And anyway this second approach does not overcome the chief drawbacks of the literature which are that we have no direct access to the evidence of others, and often face a very partial view of the assumptions made and the analyses conducted.

Near-threshold ω\omega-meson production in proton-proton collisions: With or without resonance excitations ?

We present results for the $p p \to p p \omega$ reaction studied by considering two different scenarios: with and without the inclusion of nucleon resonance excitations. The recently measured angular distribution by the COSY-TOF Collaboration at an excess energy of $Q = 173$ MeV and the energy dependence of the total cross section data for $\pi^- p \to \omega n$ are used to calibrate the model parameters. The inclusion of nucleon resonances improves the theoretical prediction for the energy dependence of the total cross section in $pp \to pp\omega$ at excess energies $Q < 31$ MeV. However, it still underestimates the data by about a factor of two, and remains a problem in understanding the reaction mechanism.Comment: Fig.5 and text modified, Latex, 4 pages, 8 embedded figures, uses espcrc1.sty (included), talk presented at PANIC02, Osaka, Japan, 30 September - 4 October 200

Calibrating Data to Sensitivity in Private Data Analysis

We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes. This paper details the data analysis platform wPINQ, which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (e.g., counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.Comment: 17 page

Big Data Dimensional Analysis

The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data variety is automatically understanding the underlying structures and patterns of the data. Such an understanding is required as a pre-requisite to the application of advanced analytics to the data. Further, big data sets often contain anomalies and errors that are difficult to know a priori. Current approaches to understanding data structure are drawn from the traditional database ontology design. These approaches are effective, but often require too much human involvement to be effective for the volume, velocity and variety of data encountered by big data systems. Dimensional Data Analysis (DDA) is a proposed technique that allows big data analysts to quickly understand the overall structure of a big dataset, determine anomalies. DDA exploits structures that exist in a wide class of data to quickly determine the nature of the data and its statical anomalies. DDA leverages existing schemas that are employed in big data databases today. This paper presents DDA, applies it to a number of data sets, and measures its performance. The overhead of DDA is low and can be applied to existing big data systems without greatly impacting their computing requirements.Comment: From IEEE HPEC 201

Longitudinal Functional Data Analysis

We consider analysis of dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated time visits and for each visit we record a functional variable. We propose a novel parsimonious modeling framework for the repeatedly observed functional variables that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed for the study of the dynamic behavior of the underlying process, and is computationally fast. Theoretical properties of this framework are studied and numerical investigation confirms excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis. Using Shiny (Chang et al., 2015) we implement interactive plots to help visualize longitudinal functional data as well as the various components and prediction obtained using the proposed method.Comment: 32 pages, 4 figure