Modul manual: Hochschule Offenburg

M. Sc. Renewable Energy and Data Engineering

Study conventional and renewable energy systems, smart grids and the underlying algorithms as well as energy efficiency measures.

Application

EMI Department

Teaching methods

Lecture/Lab

Learning target / Competences

Duration

Hours per week

10.0

Overview

ECTS

10.0

Requirements for awarding credit points

Energy Data Engineering 1 and Database Systems: paper and presentation; weight: 2/3

Energy Data Engineering 2: paper and presentation; weight: 1/3

Recommended semester

1 und 2

Frequency

annually (SS+WS)

Usability

Master RED

Lectures

Type	Lecture/lab
Nr.	M+V3049
Hours per week	4.0
Content	Data Mining Terminology and concepts Data Mining process models Exploratory Data Analysis Descriptive Statistics Classification and Regression Models (Decision Trees, Random Forest, K-nearest neighbours, Naive Bayes, ...) Model Evaluation and Comparison Clustering Linear Regression Time Series Analysis
Literature	Reddy, T. Agami, Applied data analysis and modeling for energy engineers and scientists; Springer Science & Business Media, 2011 Witten, I. H. and Hall, M. A., Data mining: Practical machine learning tools and techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011 Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed. Burlington: Elsevier Science, 2011 Hastie, T., Tibshirani, R., and Friedman, J. H., The elements of statistical learning: Data mining, inference, and prediction, 2nd ed. Springer series in statistics. New York: Springer, 2009 Alpaydın, E., Maschinelles Lernen. München: Oldenbourg, 2008.