Using smart grid data to predict next-day energy consumption and photovoltaic production

Stephan Dreiseitl, Andreas Vieider, Christoph Larch

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

The rise of sustainable energy production is a challenge for grid operators, who need to balance consumer demand with an increasingly volatile supply that is heavily dependent on weather conditions and environmental factors. Smart gird data provides fine-grained insight into consumer behavior as well as local renewable energy producers. We use data from an electric company in a region of South Tyrol to model both energy consumption as well as energy production. With a simple nearest-neighbor approach, we predict next-day load profiles for local power stations with relative error rates as low as 3 %. The energy production at these local power stations (in the form of photovoltaic power plants) can be predicted by adapting an ideal irradiation model to actual production data, stratified by varying weather conditions. Using this approach, we achieve relative errors in predicting next-day power production of 3–9% for favorable weather conditions.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
EditorsFranz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
PublisherSpringer
Pages228-235
Number of pages8
ISBN (Print)9783319273396
DOIs
Publication statusPublished - 2015
Event15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain
Duration: 8 Feb 201513 Feb 2015
http://eurocast2015.fulp.ulpgc.es/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9520
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Computer Aided Systems Theory, Eurocast 2015
CountrySpain
CityLas Palmas, Gran Canaria
Period08.02.201513.02.2015
Internet address

Keywords

  • Energy prediction
  • Photovoltaic power production
  • Smart grid

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