Time series analysis with apache spark and its applications to energy informatics

Energy Informatics

Cornelia Krome¹  Volker Sander² 

[1] Faculty of Medical Engineering and Technomathematics, Jülich, Germany

关键词: Time series;    Apache spark;    Energy informatics;    Forecast;    ARMA;   

DOI  :  10.1186/s42162-018-0043-1

学科分类：计算机网络和通讯

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY