Data Analysis and Modelling Techniques in Renewable Energy
Language of instruction
On successful completion of the course, the student should be able to
design experiments and interpret experimental data within a physics-based framework
pre-process data sets, including cleaning, filtering and clustering
apply different regression and interpolation approaches to unveil data relationships
establish and solve optimization problems
perform numerical differentiation and integration of data sets
solve numerically ordinary and partial differential equations
use software packages with powerful libraries (like Matlab) to perform R&D activities
Dimensional analysis: Buckinghams P theorem, relevant dimensionless quantities/numbers
Similarity theory: notion of scale, similarity and types of similarity
Data mining: outliers, linear and nonlinear correlation and PCA
Signal processing: FFT and data filtering
Regression and interpolation: linear and nonlinear regression, machine learning regressors, polynomial and stepwise interpolation
Optimization: linear and nonlinear programming, multiobjective optimization, global optimization
Numerical integration and differentiation: Newton-Cotes formulas, Romberg integration, adaptive quadrature methods, use of Taylor series and derivatives of data
Ordinary differential equations: Runge-Kutta methods, initial value problems, boundary value problems
Lectures, exercises and laboratory work. Estimated work load for the average student is approximately 200 hours.
Satisfactory submission of compulsory exercises/projects done in group. Information will be given in the Canvas at the beginning of the course.
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.
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.