Doing More with Less: Applying Low-Frequency Energy Data to Define Thermal Performance of House Units and Energy-Saving Opportunities

Abstract

High-frequency energy data, such as hourly and sub-hourly energy, provide various options for assessing building energy performance. However, the scarcity of such energy data is among the challenges of applying most of the existing energy analysis approaches in large-scale building energy remodeling projects. The purpose of this study is to develop a practical method to define the energy performance of residential house units using monthly energy data that are relatively easy to obtain for existing building stock. In addition, based on the defined energy use characteristics, house units are classified, and energy retrofit measures are proposed for energy-inefficient units. In this study, we applied a change-point regression model to investigate the heterogeneity in the monthly gas consumption of 200 house units sampled from four apartment complexes in Ulsan, Republic of Korea. Using a four-quadrant plane and the fitted model parameters, we identified most energy-inefficient house units and their potential energy-saving measures are assessed. The results indicate that around a 41% energy reduction through enhanced thermal properties and heating systems was achieved. The study responds to the need for a straightforward procedure for identifying and prioritizing the best targets for effective energy upgrades of existing buildings.

Keywords

• energy remodeling
• residential buildings
• change-point regression model
• building energy simulation program