ENBIS-13 in Ankara15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013
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
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
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
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
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)
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
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.