What we talk about when we talk about EEMs: using text mining and topic modeling to understand building energy efficiency measures (1836-RP)

Abstract

Energy Efficiency Measures (EEMs) play a central role throughout the building energy efficiency industry, and lists of EEMs therefore exist in a variety of resources. However, each of these use different conventions for describing and organizing measures, which presents a major challenge for aggregating information across these resources. The goal of this study is to discover trends in how existing resources describe and organize EEMs using topic modeling and other text mining methods. A unique dataset of 3,490 EEMs from 16 different documents was compiled and analyzed using frequency analysis, part of speech tagging, and topic modeling. The results showed three major trends. First, a typical EEM contains six words and is phrased in verb-noun format, although these characteristics varied widely. Second, there are words and bigrams commonly used across many EEMs, and these include action words, specific building components, broader building systems, and descriptor terms. Third, there are thematic similarities between the EEM lists, which in some cases highlight the ways in which these lists are derived from one another. These findings provide insight into the nature of EEMs and can be used as the basis for developing a standardized system for organizing and describing EEMs.

Acknowledgments

The authors gratefully acknowledge the members of the Project Monitoring Subcommittee (PMS) for their guidance and feedback: Chris Balbach (Chair), Rob Hitchcock, and Adam Hinge. Special thanks to the members of the Project Advisory Board (PAB) for generously sharing their time, technical expertise, and insight: Marco Ascazubi, Honey Berk, David Hodgins, Jim Kelsey, Nicholas Long, Paul Mathew, Ben O’Donnell, and David Sachs. Additional thanks to others who contributed their expertise at PMS and PAS meetings, especially Travis Walter and Harry Bergmann.

Data availability

The data that support the findings of this study are openly available in Zenodo at http://doi.org/10.5281/zenodo.6726629. The code used to produce this analysis is available at: https://github.com/retrofit-lab/ashrae-1836-rp-text-mining.

Additional information

Funding

This work was funded by ASHRAE through 1836-RP, Developing a Standardized Categorization System for Energy Efficiency Measures. The project was sponsored by TC 7.6 Building Energy Performance, and co-sponsored by SSPC 100 Energy Efficiency in Existing Buildings and the Building EQ Committee.