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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Energieinformatik

Lehrform Vorlesung
Lernziele / Kompetenzen

The students have an understanding of Big Data Analytics. They know the different process phases in Big Data Analytics (collection, processing, cleansing, explorative statistics, modeling, evaluation and representation of data). They know algorithm applied in the different phases and are able to select suitable methods for practical problems.

Further, students know about real time Big Data analytics. They can clearly differentiate between terms like pattern recognition, machine learning, and deep learning.
Dauer 1
SWS 8.0
Aufwand
Lehrveranstaltung 120
Selbststudium / Gruppenarbeit: 120
Workload 240
ECTS 8.0
Voraussetzungen für die Vergabe von LP

zwei Klausurarbeiten, je 90 Min. plus Laborarbeit
Leistungspunkte Noten

8 Credits
Modulverantwortlicher

Mr. Uchenna Johnpaul Aniekwensi
Haeufigkeit jedes 2. Semester
Verwendbarkeit

Master PDE
Veranstaltungen

Energieinformatik 1/Energy Data Engineering 1

Art Vorlesung/Labor
Nr. M+V3049
SWS 4.0
Lerninhalt
  • 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
Literatur

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.
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