Spatial clustering of temporal energy profiles with empirical orthogonal functions and max-p regionalization

TL;DR

This paper introduces a spatial clustering method combining empirical orthogonal functions and max-p regionalization to identify regions with similar temporal energy profiles, demonstrated on wind and solar data in Ireland and Britain.

Contribution

The paper presents a novel approach integrating EOFs and max-p regionalization for spatially coherent energy profile clustering, enhancing regional energy resource analysis.

Findings

Solar clusters are more defined at smaller land areas.

Wind and solar energy profiles can be effectively grouped spatially.

The method reveals regional differences in renewable energy potential.

Abstract

This paper presents a spatial clustering method to create regions with similar time-varying energy characteristics. This method combines empirical orthogonal functions (EOFs) for dimensionality reduction and max-p regionalization for spatial clustering. The proposed approach creates regions that each have a similar value of a spatially extensive attribute, such as available land area, population, or GDP, as well as similar weather-dependent temporal energy profiles, such as wind and solar generation potential or heating and cooling demand, within each region. We demonstrate this technique using hourly wind and solar generation potential in 2019 in Ireland and Britain. Solar generation clusters are best-defined at a smaller land area threshold compared to wind generation.