SVD-based Visualisation and Approximation for Time Series Data in Smart Energy Systems

TL;DR

This paper introduces a matrix-based visualization and approximation method for time series data in smart energy systems, leveraging SVD to reveal subtle features and underlying structures across different timescales.

Contribution

It proposes interpreting time series as images and applying matrix decomposition to enhance visualization and analysis of energy data.

Findings

Effective visualization of daily and seasonal patterns.

Identification of faint features in energy time series.

Revealed underlying structures using matrix decomposition.

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.