TY  - CHAP
A1  - Krome, Cornelia
A1  - Sander, Volker
T1  - Time series analysis with apache spark and its applications to energy informatics
T2  - Proceedings of the 7th DACH+ Conference on Energy Informatics
N2  - In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.

This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
Y1  - 2018
U6  - http://dx.doi.org/10.1186/s42162-018-0043-1
N1  - Energy Informatics 2018, Volume 1 Supplement 1
ER  - 
TY  - JOUR
A1  - Krome, Cornelia
A1  - Höft, Jan
A1  - Sander, Volker
T1  - Clustering time series applied to energy markets
JF  - Energy Informatics
Y1  - 2019
U6  - http://dx.doi.org/10.1186/s42162-019-0076-0
SN  - 2520-8942
VL  - 2
IS  - Article numer 17
SP  - 1
EP  - 20
PB  - Springer
CY  - Berlin
ER  -