M. Sc. Renewable Energy and Data Engineering

Studieren Sie konventionelle und erneuerbare Energiesysteme, intelligente Stromnetze und die zugrundeliegenden Algorithmen sowie Energieeffizienzmaßnahmen.
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Fakultät EMI

Modulhandbuch

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Energy Informatics

Teaching methods Lecture/Lab
Learning target / Competences

 

 
Duration 2
Hours per week 10.0
Overview
Classes 150
Individual / Group work: 150
Workload 300
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

Energy Data Engineering 1

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.

Database Systems

Type Lecture/lab
Nr. M+V2052
Hours per week 2.0

Energieinformatik 2

Type Lecture/lab
Nr. M+V3050
Hours per week 4.0
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