ENBIS-13 in Ankara

15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013

My abstracts


The following abstracts have been accepted for this event:

  • Quantitative Prediction and Hedging of Risk in Energy Markets

    Authors: Martin Rainer (ENAMEC Inst., FZRM Univ. Würzburg)
    Primary area of focus / application: Other: Energy Markets
    Keywords: energy markets, risk management, market risk, risk factors, hedging, extremal events, gas
    Submitted at 30-May-2013 23:27 by Martin Rainer
    Traditional and advanced methods for quantitative prediction of risk factors (e.g. amounts, prices) in energy markets are presented. We describe statistical methods and stochastic models which are both, practically applicable and theoretically founded. Techniques for prediction and management of market risk are discussed and methods for measuring risk related to extremal events are presented.
    We give examples for risk factors of gas markets in particular.
  • Learning Algorithms & Neural Networks: Forecasting Energy Resources and Markets

    Authors: Martin Rainer (ENAMEC Inst., FZRM Univ. Würzburg), Stefan Giebel (Univ. Kassel)
    Primary area of focus / application: Other: Energy Markets
    Keywords: learning algorithms, neural networks, stochastic processes, energy markets, wind, gas
    Submitted at 30-May-2013 23:50 by Martin Rainer
    Energy resources and energy markets are typically subject to uncertainty in their future dynamics. As compared to the standard regression and time series techniques, learning algorithms and neural networks are providing forecasting alternatives, which are both, more flexible and more sensitive. We extend the multi-layer perceptron (MLP) approach in order to accommodate both, data mining-type learning, where hidden layers and activation functions remain without theoretical interpretation, and a more structured learning, where each neuron has a customized activation function for a specific purpose. In particular, we consider a new neuro-stochastic MLP-prediction, where each intermediate neuron is learning one of the parameters of a stochastic process, and the output neuron is learning the prediction as a combination of them according Ito formulas for a non-linearly transformed stochastic process.
    We give examples for MLP-predictions of both types of energy resources (e.g. wind) and energy market quantities and prices (e.g. for gas and electricity).
  • Estimation of Energy Savings through a Kriging Metamodel

    Authors: Esperan Padonou (Ecole Nationale Supérieure des Mines, EMSE-FAYOL, CNRS UMR6158, LIMOS, F-42023 Saint-Etienne, 42023 Saint-Etienne Cedex 2), Jonathan Villot (Ecole Nationale Supérieure des Mines, EMSE-FAYOL, CNRS UMR6158, LIMOS, F-42023 Saint-Etienne, 42023 Saint-Etienne Cedex 2)
    Primary area of focus / application: Modelling
    Keywords: Kriging, Variables selection, noisy observations, cross validation, energy savings, ICT-based services
    Submitted at 30-May-2013 23:56 by Esperan Padonou
    18-Sep-2013 09:40 Estimation of Energy Savings through a Kriging Metamodel
    The objective of the SHOWE-IT project is to demonstrate, under real conditions, how advanced ICT components and systems can enable services that help reduce energy and water consumption in social housing across Europe. To achieve this, the project takes a demand-driven approach, prioritizing as starting point an affordable investment per dwelling, and putting in place an integrated and easy replicable ICT-based service. The project expects to achieve an overall energy and water consumption reduction of 20 %.
    In SHOWE-IT, the main scientific bottleneck is due to the lack of comparative data to estimate the savings, once the technology is put in place. Considering the context of the project, the option that fits better is the CGPG method (Control Group/Pilot Group). The CGPG consists of establishing two groups with the same kind of profile in each location. One group has intervention (pilot group) and the other (control group) have not. The savings are calculated by an indirect comparison between those two groups.

    A fully automated kriging metamodel is used to estimate the consumptions that would probably correspond to the Pilot Group if there were no ICT treatment. The challenge here is to select automatically the main socio economic variables that better explain these consumptions on the one hand, and increase the model accuracy to make the estimated savings reliable on the other hand.

    The first results on electricity consumption show an average savings lower than 10% and a confidence interval at 9%. The quantity of data (only two months) explains those results. In fact, we expect the uncertainty to decrease and we hope the estimate savings to increase on duration.

    However, to optimize the methodology, ongoing works are developed on two points: 1:) The robustness of the methodology since the data contains aberrant observations. 2:) The optimization of the variables selection phase by combining the automated criteria to field expert’s knowledge.
  • The Use of Demographic Techniques in Evaluating Age-based Data

    Authors: Samuel Adeyemo (Federal Polytechnic, Nekede)
    Primary area of focus / application: Other: Demography
    Keywords: Digit preference, Whipple’s index, Myer’s index, Age, UN age-sex accuracy index
    Submitted at 1-Jun-2013 13:22 by SAMUEL ADEYEMO
    17-Sep-2013 10:50 The Use of Demographic Techniques in Evaluating Age-based Data
    Demographic data are usually classified by age and sex, as they both are very important variables. National plans for the provision of such need as housing, food, education, health, e.t.c depend on the relevant socio - demographic statistics classified by age and sex. The importance of accurate age-sex data in demographic analysis cannot be over emphasized as there are errors associated with age and sex. As age data tend to be more inaccurate than sex data, there is need for investigation and evaluation of the quality of the data collected on age. This paper is purposely prepared to evaluate the accuracy of age reporting using the demographic and health survey 2008 for both Ghana and Nigeria using demographic techniques. The Whipples’ and Meyer’s indices as well as the United Nations Age-Sex accuracy index were determined. The result of the work has shown very accurate age data reporting for all except male at age ending with “0” in Nigeria only. For the Myer’s index, the most preferred final digits are ‘5’ and ‘0’, while the most avoided final digit by both sexes is ‘1’ in the two countries. Furthermore, the calculated age-sex accuracy index is 35.48 (for Nigeria) that qualified the age data usable with adjustment according to the United Nations scaling.
  • Assessing Spatial Patterns of Defectivity on Semiconductor Wafers: An Approach Based on the Minimum Spanning Tree Algorithm

    Authors: Riccardo Borgoni (University of Milano Bicocca), Gabriele Arici (University of Milano Bicocca)
    Primary area of focus / application: Process
    Keywords: microelectronics, defectivity, spatial clustering, minimum spanning tree
    Submitted at 2-Jun-2013 09:09 by Riccardo Borgoni
    Accepted (view paper)
    17-Sep-2013 17:30 Assessing Spatial Patterns of Defectivity on Semiconductor Wafers: An Approach Based on the Minimum Spanning Tree Algorithm
    Semiconductor industry has been getting an essential role in the economy of the modern society as well as in the day-to-day life of humans. The fabrication process adopted to create integrated circuits consists of a sequence of several physical and chemical steps performed on a thin silicon slice of a few inches of diameter, called wafer.

    In the semiconductor industry, one of the main causes of yield loss is the presence of defects on the wafer surface. Defects are not necessarily uniformly distributed on the wafer. Often, defects tend to cluster on structured patterns revealing potential problems in the manufacturing process. A prompt identification of the causes of such clusters as well as their early elimination are therefore critical.

    In many manufacturing processes, inspection of defect structures on the wafers is accomplished by visual inspection of human experts. However, this procedure is time-consuming, expensive and prone to errors. Hence, automated procedures for inspecting defectivity and identifying agglomerations of defects can be extremely appealing.

    In this paper we present an explorative method to detect the presence of systematic spatial structures in the defect locations occurring on the wafer surface based on the minimum spanning tree algorithm. Starting from the output produced by the algorithm, suitable graphical tools were developed to display a cartography of the defectivity structure. The proposed procedure proved to be effective in detecting both convex and non-convex-shaped clusters of defects in an extensive simulation study as well as in a real wafer data application.
  • MSA in the Cloud

    Authors: Magdalena Diering (Poznań University of Technology), Agnieszka Kujawińska (Poznań University of Technology), Krzysztof Dyczkowski (Adam Mickiewicz University, Poznań)
    Primary area of focus / application: Metrology & measurement systems analysis
    Keywords: measurement systems analysis, cloud computing, control, web application
    Submitted at 2-Jun-2013 19:51 by Magdalena Diering
    17-Sep-2013 17:50 MSA in the Cloud
    The role of an IT system has changed from passive to active: a human being poses a problem, whereas a system looks for its solution. The recent years have seen a dynamic development in Internet and mobile technologies based on the so-called cloud computing.
    Thus, the current state of knowledge within Measurement Systems Analysis area and the currently used IT solutions have directed the authors to formulate an overriding aim behind their research work – to create a web application used for statistical analysis of measurement systems which will be based on Internet website that will use a browser in a presentation server.
    Authors will present a new approach to MSA analysis - with usage of cloud computing - and its advantages and challenges which are to be discussed. Special attention will be placed on the key dimensions of innovation of MSA in the cloud idea.