Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
What we talk about when we talk about EEMs: using text mining and topic modeling to understand building energy efficiency measures (1836-RP)
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.
What we talk about when we talk about EEMs: using text mining and topic modeling to understand building energy efficiency measures (1836-RP)
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.
What we talk about when we talk about EEMs: using text mining and topic modeling to understand building energy efficiency measures (1836-RP)
Khanuja, Apoorv (Autor:in) / Webb, Amanda L. (Autor:in)
Science and Technology for the Built Environment ; 29 ; 4-18
02.01.2023
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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