ENE418 Data Analysis and Modelling Techniques in Renewable Energy
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leaders:
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- Rune Strandberg
- Joao Gouveia Aparicio Bento Leal
- Lecture Semester:
- Spring
- Teaching language:
- English
- Duration:
- 1 term
Teaching language
EnglishCourse contents
-
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Dimensional analysis: Buckinghams P theorem, relevant dimensionless quantities/numbers
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Similarity theory: notion of scale, similarity and types of similarity
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Data mining: outliers, linear and nonlinear correlation and PCA
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Signal processing: FFT and data filtering
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Regression and interpolation: linear and nonlinear regression, machine learning regressors, polynomial and stepwise interpolation
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Optimization: linear and nonlinear programming, multiobjective optimization, global optimization
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Numerical integration and differentiation: Newton-Cotes formulas, Romberg integration, adaptive quadrature methods, use of Taylor series and derivatives of data
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Ordinary differential equations: Runge-Kutta methods, initial value problems, boundary value problems
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Partial differential equations: finite difference methods, elliptic, parabolic and hyperbolic PDEs
Learning outcomes
On successful completion of the course, the student should be able to
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design experiments and interpret experimental data within a physics-based framework
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pre-process data sets, including cleaning, filtering and clustering
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apply different regression and interpolation approaches to unveil data relationships
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establish and solve optimization problems
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perform numerical differentiation and integration of data sets
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solve numerically ordinary and partial differential equations
use software packages with powerful libraries (like Matlab) to perform R&D activities
Examination requirements
Satisfactory submission of compulsory exercises/projects done in group. Information will be given in the Canvas at the beginning of the course.
Teaching methods
Lectures, exercises and laboratory work. Estimated work load for the average student is approximately 200 hours.
Evaluation
The study programme manager decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.
Offered as Single Standing Module
Yes, if there are places available.
Assessment methods and criteria
Portofolio. Group graded assessment. The group as a whole is graded. Further information about contents and weighting of the exercises/projects will be given in Canvas at the beginning of the semester.