ENBIS-17 in Naples9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017
The following abstracts have been accepted for this event:
Optimization of Renewable Energy Sources for a Data Center Using Energy Storage
Authors: Christophe Varnier (FEMTO-ST institute - ENSMM - UBFC), Jean-Marc Nicod (FEMTO-ST institute - ENSMM - UBFC)
Primary area of focus / application: Other: Machine learning for energy management
Keywords: Optimization, Renewable energy, Datacenter, Mixed integer linear programming
Submitted at 13-Apr-2017 11:31 by Christophe VARNIER
Accepted (view paper)
With depleting conventional energy resources, whole world is now looking for alternatives to meet energy demands. One of the solutions is renewable energy which is non-exhaustible and non-polluting, but problem is intermittent nature of these energies.
In this communication, we propose to study the management of a hybrid energy source system that deliver power for a datacenter. The system is composed of several energy technologies and back-up as well as energy storage units. The storage devices can be a battery bank, supercapacitor bank, or a fuel cell-electrolyzer system. Other energy sources are considered such as wind turbine and/or photovoltaic panels.
The problem addressed in this work is a hybrid renewable energy system and the point is to manage the energy production system with the objective to satisfy a load demand and to minimize the power exchanged with the grid. A mixed integer linear program is proposed to solve the addressed problem.
Estimating the Size of a Defective Subgroup in Industrial Mass Production
Authors: Benjamin Sobotta (Robert Bosch GmbH)
Primary area of focus / application: Other: Sampling
Keywords: Defective subgroup, Industrial production, Risk analysis, Sampling
Submitted at 13-Apr-2017 16:08 by Lennart Kann
Consequently, the estimation of the affected volume is paramount during risk analysis. We propose an approach to determine the size of this defective subgroup. At the core of our so-called Test Gate Method lies production control data. Given that this data is readily available, it is leveraged to quickly and reliably obtain the size of the subpopulation of interest. Because there are otherwise few dependencies or limitations, our method may be applied to a broad spectrum of cases. This especially holds true when the subpopulation comprises only few parts per million.
The Test Gate Method has been successfully applied on a number of occasions. We will present two representative cases to illustrate the approach and highlight its benefits.
Wind Turbine Performance Decline in Sweden
Authors: Jesper Rydén (Uppsala University), Jon Olauson (Uppsala University)
Primary area of focus / application: Reliability
Secondary area of focus / application: Modelling
Keywords: Wind power, Decline, Multiple regression, Bootstrap
Submitted at 13-Apr-2017 17:15 by Jesper Rydén
Accepted (view paper)
A Fresh Look at Effect Aliasing and Interactions: Some New Wine in Old Bottles
Authors: Jeff Wu (Georgia Tech)
Primary area of focus / application: Other: Box medal lecture
Secondary area of focus / application: Design and analysis of experiments
Keywords: Conditional main effects, Fractional factorial designs, Nonregular designs, Orthogonal arrays
Submitted at 14-Apr-2017 01:33 by Jeff Wu
Statistical Intervals for Predictions of Multiple Future Observations
Authors: Bernard G Francq (GSK), Stéphane Laurent (GSK), Dan Lin (GSK), Walter Hoyer (GSK)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Quality
Keywords: Prediction interval, Prediction region, Multiple predictions (multiplicity issues), Automated application, Reproducible report
Submitted at 14-Apr-2017 15:06 by Bernard Francq
Classical statistical tests (t-tests, equivalence tests etc.) are not appropriate for use in this case due to the small number of new batches tested. As a necessary requirement for demonstrating comparability throughout the change, it is, therefore, assessed that the individual values of the new batches should lie within the prediction interval computed from the historic batches.
In this presentation, we generalize the concept of prediction intervals to a prediction region for multiple future observations in line with the Gaussian multivariate distribution. An analogy is drawn between inference and prediction, and between the multiplicity issue in the comparison of multiple means and the prediction region for multiple future observations. Different statistical procedures are reviewed and applied for construction of a prediction region, including approximate and exact methods (i.e. Bonferroni, Dunnett, Holm, Sidak,…).
An automated application with graphical user interface based on R will be presented. The application allows upload of data and returns a pdf report within seconds. Also other common intervals (confidence intervals for the mean, tolerance intervals, etc.) can be obtained with the tool.
Issues in the Revision of ISO 3951-1
Authors: Rainer Göb (University of Würzburg)
Primary area of focus / application: Quality
Keywords: Statistical standard, Acceptance sampling, Proportion nonconforming, Variables sampling
Submitted at 15-Apr-2017 00:47 by Rainer Göb