Leveraging smart meter data and Monte Carlo simulation for peak power demand estimation in multi-apartment buildings

Abstract

Accurately estimating electrical capacity requirements in apartment buildings is a complex challenge, influenced by factors such as occupant behaviour, appliance usage, and varying consumption profiles. This study investigates peak power demand in 8,800 apartments across 175 residential buildings in Pirkanmaa region, Finland, using hourly consumption data of seven years and the Monte Carlo method for sampling. The apartments were categorized by type, including variations with and without electric sauna heaters. The analysis found that while peak consumption varies significantly between individual apartments, grouping apartments together reduces variation in peak demand. As the number of apartments increases, the aggregated peak demand per apartment decreases, especially in larger apartment groups. The study highlights the significant impact of saunas on both peak power demand and timing, with apartments containing saunas showing more predictable peak times. The findings provide valuable insights for accurately sizing electrical connections in multi-apartment buildings, particularly when considering varying apartment configurations. This study underscores the need to update existing peak-power evaluation methodologies to reflect recent changes in residential energy use, offering a more accurate basis for future electrical infrastructure planning.