KIT | KIT-Bibliothek | Impressum | Datenschutz

Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments

Shahoud, Shadi 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Applying Machine Learning (ML) manually to a given problem setting is a tedious and time-consuming process which brings many challenges with it, especially in the context of Big Data. In such a context, gaining insightful information, finding patterns, and extracting knowledge from large datasets are quite complex tasks. Additionally, the configurations of the underlying Big Data infrastructure introduce more complexity for configuring and running ML tasks. With the growing interest in ML the last few years, particularly people without extensive ML expertise have a high demand for frameworks assisting people in applying the right ML algorithm to their problem setting. This is especially true in the field of smart energy system applications where more and more ML algorithms are used e.g. for time series forecasting. Generally, two groups of non-expert users are distinguished to perform energy time series forecasting. The first one includes the users who are familiar with statistics and ML but are not able to write the necessary programming code for training and evaluating ML models using the well-known trial-and-error approach. Such an approach is time consuming and wastes resources for constructing multiple models. ... mehr


Volltext §
DOI: 10.5445/IR/1000154568
Veröffentlicht am 23.01.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Hochschulschrift
Publikationsdatum 23.01.2023
Sprache Englisch
Identifikator KITopen-ID: 1000154568
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xviii, 179 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Automation und angewandte Informatik (IAI)
Prüfungsdatum 02.12.2022
Schlagwörter Machine Learning, Data Mining, Time Series, Energy Forecasting, Automated Model Selection, Meta Learning
Referent/Betreuer Hagenmeyer, Veit
Steinke, Florian
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page