Data mining of space heating system performance in affordable housing

Ren, Xiaoxin; Yan, Da; Hong, Tianzhen

doi:10.1016/j.buildenv.2015.02.009

Title: Data mining of space heating system performance in affordable housing

Journal Article · Mon Feb 16 00:00:00 EST 2015 · Building and Environment

DOI:https://doi.org/10.1016/j.buildenv.2015.02.009· OSTI ID:1253721

Ren, Xiaoxin ^[1]; Yan, Da ^[1]; Hong, Tianzhen ^[2]

Tsinghua Univ., Beijing (China)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. In conclusion, from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1253721

Alternate ID(s):: OSTI ID: 1247779

Report Number(s):: LBNL-180239; ir:180239

Journal Information:: Building and Environment, Vol. 89, Issue C; ISSN 0360-1323

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 52 works

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Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches Wang, Junqi; Liu, Rundong; Zhang, Linfeng Energies, Vol. 12, Issue 20 https://doi.org/10.3390/en12203863	journal	October 2019

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97 MATHEMATICS AND COMPUTING
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
data mining
space heating
affordable housing
occupant behavior
building simulation
clustering
decision tree

Title: Data mining of space heating system performance in affordable housing

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References (24)

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