Adaptation and Thermal Environment

Jendritzky, Gerd; Dear, Richard de

doi:10.1007/978-1-4020-8921-3_2

Cited by 33 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, it is proven that humans perceive comfort in a range of environmental thermal conditions [8]. In addition, many dynamic environment related variables (e.g., weather [8]) and human related variables (e.g., acclimation [9]) effect thermal comfort and therefore, the individuals' thermal comfort ranges change over time [10][11][12][13]. Given the range of comfortable conditions for an occupant, we can potentially control a service system to provide thermal conditions in that range while minimizing the overall energy consumption [6,7].…”

Section: Introductionmentioning

confidence: 99%

Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings

et al. 2016

View full text Add to dashboard Cite

This paper provides a systematic approach for quantifying the influence of building size, construction category, climate, occupancy schedule, setpoint, and deadband on HVAC energy consumption in office buildings. Simulating the DOE reference office buildings of three sizes and three construction categories in all United States climate zones, using the EnergyPlus, we conducted several N-way ANOVA analyses to study the interrelationships between setpoints, deadbands and several building related and environment related factors. In summary, daily optimal deadband selection of 0, 1, 2, 4, 5, and 6 K would result in an average energy savings of -70.0, -34.9, -13.7, 9.6, 16.4, and 21.2 %, respectively, compared to baseline deadline of 3 K. Selecting the daily optimal setpoint in the range of 22.5 ± 1 °C, 22.5 ± 2 °C, and 22.5 ± 3 °C would result in an average savings of 7.5, 12.7, and 16.4 %, respectively, compared to the baseline setpoint of 22.5 °C. Additionally, we found that when the outdoor temperature is within -20 to 30°C, the optimal setpoint depends on the building size. We also observed a range of outdoor temperatures (e.g., 9 -14 °C for small buildings and 8 -11 °C for medium buildings) where the setpoint selection would only slightly influence the energy consumption. However, the choice of setpoints becomes very influential (up to 30% of energy savings) where the outdoor temperatures are slightly outside the mentioned ranges on either direction. The potential savings from selecting daily optimal setpoints in the range of 22.5 ± 3 °C in different climates and for small, medium and large office buildings, would lead to 10.09 -37.03%, 11.43 -21.01%, and 6.78 -11.34 % savings, respectively, depending on the climate.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings

et al. 2016

View full text Add to dashboard Cite

show abstract

“…At present, the response of indoor temperature and humidity conditions to outdoor variability, and the heterogeneity of that response among different indoor environments are largely unknown (Jendritzky and Dear 2009; White-Newsome et al 2012). The predominant use of outdoor weather and climate data for studying human health outcomes is likely due to the widespread availability of such data and the relative paucity of existing indoor environmental data records.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic and Outdoor Meteorological Determinants of Indoor Temperature and Humidity in New York City Dwellings*

Tamerius

Perzanowski

Acosta

et al. 2013

Weather, Climate, and Society

View full text Add to dashboard Cite

Background-Numerous mechanisms link outdoor weather and climate conditions to human health. It is likely that many health conditions are more directly affected by indoor rather than outdoor conditions. Yet, the relationship between indoor temperature and humidity conditions to outdoor variability, and the heterogeneity of the relationship among different indoor environments are largely unknown.Methods-We use 5-14 day measures of indoor temperature and relative humidity from 327 dwellings in New York City for the years 2008-2011 to investigate the relationship between indoor climate, outdoor meteorological conditions, socioeconomic conditions, and building descriptors. Study households were primarily middle-income and located across the boroughs of Brooklyn, Queens, Bronx, and Manhattan.Results-Indoor temperatures are positively associated with outdoor temperature during the warm season and study dwellings in higher socioeconomic status neighborhoods are significantly cooler. During the cool season, outdoor temperatures have little effect on indoor temperatures; however, indoor temperatures can range more than 10 °C between dwellings despite similar outdoor temperatures. Apartment buildings tend to be significantly warmer than houses and dwellings on higher floors are also significantly warmer than dwellings on lower floors. Outdoor specific humidity is positively associated with indoor specific and relative humidity, but there is no consistent relationship between outdoor and indoor relative humidity. Conclusions-InNew York City, the relationship between indoor and outdoor temperature and humidity conditions vary significantly between dwellings. These results can be used to inform studies of health outcomes for which temperature or humidity is an established factor affecting human health and highlights the need for more research on the determinants of indoor climate.

show abstract

“…Research by epidemiologists and climatologists has grown rapidly following the European heat waves in 2003 (Schär et al, 2004;Carson et al, 2006;Laaidi et al, 2006;Confalonieri et al, 2007;Gosling et al, 2007Gosling et al, , 2009aGosling et al, , 2009bMatthies et al, 2008;Basu, 2009;Jendritzky and de Dear, 2009;Menne and Matthies, 2009;Tobías et al, 2010). That summer many western European countries experienced dramatic death tolls, and temperatures were considered as 'a shape of things to come' (Beniston, 2004).…”

Section: Introductionmentioning

confidence: 99%

Heat‐related mortality in Moldova: the summer of 2007

Corobov¹,

Sheridan

Opopol

et al. 2012

Intl Journal of Climatology

View full text Add to dashboard Cite

Heat waves of 2007 in Chisinau (Moldova) were used to study the relationship between elevated temperatures and excess mortality caused by these events. As reference information, daily temperature and mortality data for an 8-year reference period (2000-2008 without 2007) were used. Mean (T mean ), maximum (T max ) and minimum (T min ) daily air temperatures, and corresponding apparent temperatures (ATs) in the warm season (April to September) were correlated with excess total mortality in 2007, taken as the difference of daily death counts or their 7-d moving averages with those of the reference period. Observed excess mortality was totalled about 190-200 deaths or 6.5-6.9% of the reference mortality. The average daily excess deaths above the threshold temperatures (TTs), in terms of a used 'estimator' (a temperature variable), were in the range of 2.0-4.4% per 1• C. TTs were identified as the lowest 2 • C class intervals above which the excess mortality rates began a sharp increase from their zero reference value. For T mean , T max and T min they were estimated as ∼25, 31 and 19 • C; TTs for ATs were somewhat lower. The heat waves were defined as a continuous period satisfying three conditions for daily T max : (1) it is above the 99th percentile of its reference distribution (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) for at least three consecutive days, (2) its average value is equal to at least this percentile for the entire period and (3) all daily values are above 90th percentile for the entire period. On the whole, eight heat events caused 146 excess deaths or about 73-77% of their total number in the warm period of 2007. Temperature-excess mortality relationships become stronger with an increasing time lag; maximal effects were revealed on the second-third days for T mean and T max , and on the first-second days for T min . The total effect of mortality displacement was estimated as about 17-25% of 'positive' excess deaths.

show abstract

Adaptation and Thermal Environment

Cited by 33 publications

References 27 publications

Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings

Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings

Socioeconomic and Outdoor Meteorological Determinants of Indoor Temperature and Humidity in New York City Dwellings*

Heat‐related mortality in Moldova: the summer of 2007

Contact Info

Product

Resources

About