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

Modul manual

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Energy Data Engineering

Teaching methods Lecture
Learning target / Competences

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.

 
Duration 1
SWS 8.0
Overview
Classes 120
Individual / Group work: 120
Workload 240
ECTS 8.0
Requirements for awarding credit points

two written exams 90 minutes plus lab work
Credits and grades

8 ECTS
Responsible person

Mr. Uchenna Johnpaul Aniekwensi 
Frequency Every 2nd sem.
Usability

Master RED
Lectures

Energieinformatik 1/Energy Data Engineering 1

Type Vorlesung/Labor
Nr. M+V3049
SWS 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.
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