Optimising Parameters in Recurrence Quantification Analysis of Smart Energy Systems

TL;DR

This paper introduces two novel methods for optimizing parameters in Recurrence Quantification Analysis by considering multiple RQA variables simultaneously, demonstrated through dynamical system and energy consumption case studies.

Contribution

The paper proposes two new multi-variable RQA parameter optimization methods, improving upon existing single-variable approaches for better analysis of complex systems.

Findings

Methods yield plausible RQA parameters for energy data

Approach successfully detects events and phase transitions

Applicable to diverse dynamical systems

Abstract

Recurrence Quantification Analysis (RQA) can help to detect significant events and phase transitions of a dynamical system, but choosing a suitable set of parameters is crucial for the success. From recurrence plots different RQA variables can be obtained and analysed. Currently, most of the methods for RQA radius optimisation are focusing on a single RQA variable. In this work we are proposing two new methods for radius optimisation that look for an optimum in the higher dimensional space of the RQA variables, therefore synchronously optimising across several variables. We illustrate our approach using two case studies: a well known Lorenz dynamical system, and a time-series obtained from monitoring energy consumption of a small enterprise. Our case studies show that both methods result in plausible values and can be used to analyse energy data.