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

2023/08/23 by Claire Halloran, Halloran, Claire, Malcolm McCulloch +1
Environmental Science · Social Sciences · #Atmospheric and Environmental Gas Dynamics #Environmental Impact and Sustainability #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social Acceptance of Renewable Energy

paper · pdf · doi:10.48550/arxiv.2308.12274

openalex publication_date 2023/08/23 · openalex created_date 2023/08/25 · openalex updated_date 2026/07/28

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.

Related

• A comparison of clustering methods for the spatial reduction of renewable electricity optimisation models of Europe
• Clustering high dimensional meteorological scenarios: results and\n performance index
• Clustering Methods Assessment for Investment in Zero Emission\n Neighborhoods Energy System
• Hull Clustering with Blended Representative Periods for Energy System Optimization Models
• Two-level Solar Irradiance Clustering with Season Identification: A Comparative Analysis