Pattern sequence-based energy demand forecast using photovoltaic energy records

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Considering recent trends in energy technology development, consumer's energy demand could be influenced by the renewable energy supply in any way. A simple extension of pattern sequence-based forecasting (PSF) enables us to predict demand curves based on the correlated bidimensional time-series by using co-occurrence patterns of energy supply and demand. However, prediction accuracy of PSF deeply depends on the clustering result, which is used for pattern matching. In this paper, a promising clustering method based on nonnegative tensor factorization is applied for this task and evaluated experimentally from the viewpoint of prediction accuracy.

Original languageEnglish
Title of host publication2012 International Conference on Renewable Energy Research and Applications, ICRERA 2012
DOIs
Publication statusPublished - 2012 Dec 1
Event1st International Conference on Renewable Energy Research and Applications, ICRERA 2012 - Nagasaki, Japan
Duration: 2012 Nov 112012 Nov 14

Publication series

Name2012 International Conference on Renewable Energy Research and Applications, ICRERA 2012

Conference

Conference1st International Conference on Renewable Energy Research and Applications, ICRERA 2012
CountryJapan
CityNagasaki
Period12/11/1112/11/14

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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  • Cite this

    Fujimoto, Y., & Hayashi, Y. (2012). Pattern sequence-based energy demand forecast using photovoltaic energy records. In 2012 International Conference on Renewable Energy Research and Applications, ICRERA 2012 [6477299] (2012 International Conference on Renewable Energy Research and Applications, ICRERA 2012). https://doi.org/10.1109/ICRERA.2012.6477299