A Comprehensive Indoor Environment Dataset from Single-Family Houses in the US

Sheik Murad Hassan Anik (), Xinghua Gao and Na Meng
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Sheik Murad Hassan Anik: Department of Computer Science, Auburn University at Montgomery, Montgomery, AL 36117, USA
Xinghua Gao: Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24061, USA
Na Meng: Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA

Data, 2025, vol. 10, issue 3, 1-14

Abstract: The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data were collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining to a total over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes.

Keywords: indoor environment dataset; remote sensing; IoT data collection; distributed data infrastructure (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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