Dealing with uncertainty: An empirical study on the relevance of renewable energy forecasting methods

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Robert Ulbricht - , Robotron Datenbank-Software GmbH (Autor:in)
  • Anna Thoß - , Hochschule für Technik und Wirtschaft (HTW) Dresden (Autor:in)
  • Hilko Donker - , Robotron Datenbank-Software GmbH (Autor:in)
  • Gunter Gräfe - , Hochschule für Technik und Wirtschaft (HTW) Dresden (Autor:in)
  • Wolfgang Lehner - , Professur für Datenbanken (Autor:in)

Abstract

The increasing share of fluctuating renewable energy sources on the world-wide energy production leads to a rising public interest in dedicated forecasting methods. As different scientific communities are dedicated to that topic, many solutions are proposed but not all are suited for users from utility companies. We describe an empirical approach to analyze the scientific relevance of renewable energy forecasting methods in literature. Then, we conduct a survey amongst forecasting software providers and users from the energy domain and compare the outcomes of both studies.

Details

OriginalspracheEnglisch
TitelData Analytics for Renewable Energy Integration
Redakteure/-innenZeyar Aung, Stuart Madnick, Oliver Kramer, Wei Lee Woon
Herausgeber (Verlag)Springer, Berlin [u. a.]
Seiten54-66
Seitenumfang13
ISBN (Print)9783319509464
PublikationsstatusVeröffentlicht - 2017
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Computer Science, Volume 10097
ISSN0302-9743

Konferenz

Titel4th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2016
Dauer23 September 2016
StadtRiva del Garda
LandItalien

Externe IDs

ORCID /0000-0001-8107-2775/work/142253529

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Machine learning, Practical relevance, Renewable energy forecasting