Multivariate Empirical Mode Decomposition and Recurrence Quantification for the Multiscale, Spatiotemporal Analysis of Electricity Demand—A Case Study of Japan

Abstract

In the new energy systems’ modeling paradigm with high temporal and spatial resolutions, the complexity of renewable resources and demand dynamics is a major obstacle for the scenario analysis of future energy systems and the design of sustainable solutions. Most advanced models are indeed currently restricted by past temporal energy demand data, improper for the analysis of future systems and often insufficient in terms of quantity or spatial resolution. A deeper understanding on energy demand dynamics is thus necessary to improve energy system models and expand their possibilities. The present study introduces noise-assisted multivariate empirical mode decomposition and recurrence quantification analysis for the study of this problematic variable with a case study of Japan’s electricity demand data per region. These tools are adapted to nonlinear, complex systems’ data and are already applied in a wide range of scientific fields including climate studies. The decomposition of electricity demand as well as the detection of irregularities in its dynamics allow to identify relations with temperature variations, demand sector shares, life style and local culture at different temporal scales.