Session: 17-01: Poster Presentations

Paper Number: 169468

169468 - Instrumentation of a Manufactured Home Community to Verify Benefits of Electrification and Retrofits 

Abstract: 

The electrification and retrofit of buildings and communities are crucial steps to meeting carbon emission goals in the current energy transition. In addition, these steps can help to reduce energy costs, improve resiliency, and improve indoor air quality. These changes are even more urgently needed in low-income communities, including some manufactured home communities, where energy burdens (the percentage of household income spent on energy bills) are typically high. However, the process of electrifying and installing efficiency retrofits can be costly for residents if each home is not prescribed with an individualized retrofit plan. Thus, we are conducting home/community energy simulations that are specifically tailored to each home in our test case community (~30 manufactured homes in Colorado) to confirm that the electrification and retrofitting process will be beneficial before the actual retrofit begins.

This study builds on previous work where we established a community simulation workflow that combines the functions of BEopt (building modeling), URBANopt (community modeling), and REopt (distributed energy resource modeling) to holistically model a manufactured home community and predict the effects and benefits of electrification and energy retrofit. Here we build on that workflow by validating simulation results at a building level with field data recorded at the breaker level. We work to quantify how granular data gathered from an actual community site improves the accuracy of a building/community model. We also compare utility and field data with simulation results regarding electrical consumption, energy costs, and zone temperatures.

In this poster presentation, we focus on the instrumentation of this community, which allows us to validate our modeling techniques. In this test case community, we have installed both electric and air quality sensors to quantify how electrification and energy retrofits improve both facets of each home’s performance. We installed electric sensors in the breaker panels of each home, where they monitor each individual circuit at a minutely timestep. We then set up air quality monitoring sensors in each home which record temperature, relative humidity, particulate matter, and radon. So far, these sensors have collected this data for over a year, which has allowed us to complete a preliminary validation of our simulated model.

With this experimental field data, we have matched electric usage field results with simulated results within 3% monthly mean bias error and 2% monthly coefficient of variation root mean square error. When compared to models made with only energy assessment data, these results improve the accuracy of our minutely electric usage models by 36% in monthly mean bias error, and 24% in monthly coefficient of variation root mean square error. Overall, we find that calibrating models using field data rather than publicly available data or home energy assessments significantly increases the accuracy of community and individual home models.

Presenting Author: Karlyle Munz Colorado School of Mines

Presenting Author Biography: Karlyle Munz is a PhD candidate in Mechanical Engineering at Colorado School of Mines. His work includes the modeling and simulation of community-scale electrification efforts in low-income communities. His PhD focuses on the creation of a workflow to model various outcomes of electrification including energy costs, operational emissions, and impact on local electricity grids.