SVD-based visualisation and approximation for time series data in smart energy systems

Abdolrahman Khoshrou, Andre B. Dorsman, Eric J. Pauwels

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Many time series in smart energy systems exhibit two different timescales. On the one hand there are patterns linked to daily human activities. On the other hand, there are relatively slow trends linked to seasonal variations. In this paper we interpret these time series as matrices, to be visualized as images. This approach has two advantages: First of all, interpreting such time series as images enables one to visually integrate across the image and makes it therefore easier to spot subtle or faint features. Second, the matrix interpretation also grants elucidation of the underlying structure using well-established matrix decomposition methods. We will illustrate both these aspects for data obtained from the German day-ahead market.

Original languageEnglish
Title of host publication2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538619537
DOIs
Publication statusPublished - 16 Jan 2018
Event2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Torino, Italy
Duration: 26 Sep 201729 Sep 2017

Conference

Conference2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017
CountryItaly
CityTorino
Period26/09/1729/09/17

Keywords

  • Data analysis
  • Data preprocessing
  • Renewable energy sources
  • Smart grids
  • Time series analysis

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