Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2017

Abstract

This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6\% more accurate for primary building use type, 24.3\% more accurate for performance class, and 63.6\% more accurate for building operations type as compared to baselines.

Keywords

Data mining, Building performance, Performance classification, Energy efficiency, Smart meters

Discipline

Energy Policy | Engineering

Research Areas

Integrative Research Areas

Publication

Energy and Buildings

Volume

156

Issue

Supplement C

First Page

1

Last Page

14

ISSN

0378-7788

Identifier

10.1016/j.enbuild.2017.09.056

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.enbuild.2017.09.056

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