Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey

Özbalta, Türkan Göksal; Sezer, Alper; Yıldız, Yusuf

doi:10.1177/1420326x11422163

Cited by 49 publications

(21 citation statements)

References 20 publications

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“…Some studies have used centralized outdoor monitoring data as a surrogate measure of residential exposure, but recent findings show that outdoor measures may be poor indicators of indoor conditions. (12-14) Direct indoor assessment may be necessary to accurately characterize residential temperature and humidity exposures.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Single-Point and Continuous Sampling Methods for Estimating Residential Indoor Temperature and Humidity

Johnston

Magnusson

Eggett

et al. 2015

Journal of Occupational and Environmental Hygiene

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Residential temperature and humidity are associated with multiple health effects. Studies commonly use single-point measures to estimate indoor temperature and humidity exposures, but there is little evidence to support this sampling strategy. This study evaluated the relationship between single-point and continuous monitoring of air temperature, apparent temperature, relative humidity, and absolute humidity over four exposure intervals (5-min, 30-min, 24-hrs, and 12-days) in 9 northern Utah homes, from March – June 2012. Three homes were sampled twice, for a total of 12 observation periods. Continuous data-logged sampling was conducted in homes for 2-3 wks, and simultaneous single-point measures (n = 114) were collected using handheld thermo-hygrometers. Time-centered single-point measures were moderately correlated with short-term (30-min) data logger mean air temperature (r = 0.76, β = 0.74), apparent temperature (r = 0.79, β = 0.79), relative humidity (r = 0.70, β = 0.63), and absolute humidity (r = 0.80, β = 0.80). Data logger 12-day means were also moderately correlated with single-point air temperature (r = 0.64, β = 0.43) and apparent temperature (r = 0.64, β = 0.44), but were weakly correlated with single-point relative humidity (r = 0.53, β = 0.35) and absolute humidity (r = 0.52, β = 0.39). Of the single-point RH measures, 59 (51.8%) deviated more than ±5%, 21 (18.4%) deviated more than ±10%, and 6 (5.3%) deviated more than ±15% from data logger 12-day means. Where continuous indoor monitoring is not feasible, single-point sampling strategies should include multiple measures collected at prescribed time points based on local conditions.

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Section: Introductionmentioning

confidence: 99%

Comparison of Single-Point and Continuous Sampling Methods for Estimating Residential Indoor Temperature and Humidity

Johnston

Magnusson

Eggett

et al. 2015

Journal of Occupational and Environmental Hygiene

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“…tempered stable random variables, L t = max{n ∈ N : Θ n ≤ t} and the distribution of jumps X j is defined in (2). Observe that, in contrast to the classical CTRW assuming that the waiting times and the jumps are independent, here the distribution of the jump depends on the actual time.…”

Section: Continuous Time Random Walkmentioning

confidence: 99%

“…The assessment of thermal environment is often derived for steady state conditions or for minor fluctuations of one or more of the variables [2]. However thermal conditions inside rooms are seldom steady, due to the interaction between building structure, meteorology, occupancy, and heat, ventilation and air conditioning (HVAC) system [3].…”

mentioning

confidence: 99%

Stochastic Modeling of Indoor Air Temperature

et al. 2013

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Temperature is one of the main parameters describing thermal comfort and indoor air quality. In this paper we propose an approach, based on a modification of the continuous time random walk, to model the indoor air temperature. We perform a statistical analysis of the recorded time series, that allows us to point out the main statistical properties of the recorded variable. The obtained conclusions about the nature of the process lead to a continuous time random walk, that in contrast to the classical approach, models time dependence of the jumps distribution. Moreover, we show that the waiting times can be modeled by a tempered stable distribution, which yields a subdiffusive behavior in short times and diffusive behavior in longer times. Finally, by conducting a simulation study we illustrate possible applications of the presented approach in the thermal comfort monitoring and forecasting.

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“…With the continuous improvement of building energy efficiency methods, enterprises or individuals have higher requirements for the accuracy of the building environment. On the one hand, in order to provide a more comfortable working environment and living space and reduce energy consumption, it is necessary to consider the appropriate temperature and humidity factors [3]. On the other hand, many companies have higher requirements for the temperature and humidity of the building environment.…”

Section: Introductionmentioning

confidence: 99%