Experimental determination of parameters in models of indoor air temperature response to reduction in supplied energy

Zawada, Bernard

doi:10.1177/1744259116647912

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The most commonly used software tools for building performance simulation (e.g. EnergyPlus or TRNSYS) require extensive computational power and a large number of assumptions about the building's geometry, its characteristic physical properties, its environment and its occupancy (Zawada, 2017). In addition, many of these inputs end up being assumptions based on the experience and intuition of professional experts.…”

Section: Discussionmentioning

confidence: 99%

Estimating time constants for over 10,000 residential buildings in North America: towards a statistical characterization of thermal dynamics

John¹,

Vallianos²,

Candanedo³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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Understanding the dynamic response of a building is essential in the design of sustainable energy-efficient buildings. Using data from over 10,000 smart thermostats, this study identifies patterns in the dynamic thermal response of residential buildings in Canada and the United States (US). The data set consists of one year of measurements recorded at 5-minute intervals for the indoor and outdoor air temperature as well as HVAC equipment run times. This study focuses on identifying effective values of time constants for the houses by applying the following procedure. First, periods complying with the following basic criteria are identified: a) the house is under free-floating conditions (i.e. when the HVAC system is switched off) for more than three hours and b) the outdoor temperature remains approximately constant (the outdoor temperature change is smaller than or equal to 2°C). Second, for each identified period, time constant values are determined by tracking the temperature responses of the house. These values are determined assuming the characteristic exponential decay of a firstorder resistance-capacitance (RC) thermal model. Finally, a statistical analysis is applied to identify a typical range of effective time constant values according to month. Consequently, calculations show significant differences between estimated values for the summer and winter months, which may be attributed to occupant behaviour. In winter, the majority of time constants range from 15 to 55 hours. In summer, most of time constants vary between less than 1 hour and 18 hours due to occupants opening windows. In addition, the dependence of the time constant on the age of the home is investigated.

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Section: Discussionmentioning

confidence: 99%

Estimating time constants for over 10,000 residential buildings in North America: towards a statistical characterization of thermal dynamics

John¹,

Vallianos²,

Candanedo³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

View full text Add to dashboard Cite

show abstract

“…In a room with a smaller density of people, the correlation disorders amount to almost 20%. In such rooms, the HVAC system has a large impact on the discrepancy, the heating system [29][30][31][32]. However, the authors [9] stated that the heating system does not affect the humidity in the room.…”

Section: Discussionmentioning

confidence: 99%

Correlation of humidity and temperature in conference rooms - preliminary tests

Gładyszewska-Fiedoruk

2018

E3S Web Conf.

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In three different conference rooms, temperature and humidity were measured, and then the correlation of these measured parameters was determined. The analysed rooms are located in buildings of the same construction and in the same town, in the temperate climate zone. The rooms had a different number of people. The experiment caused more questions than answers. The only concrete conclusions that can be formulated are that in two rooms the humidity was far too low. The HVAC system slightly disturbs the correlation of temperature and humidity. The density of people in the room affects the increase of humidity. Despite the use of air temperature controllers in all the analysed conference rooms, the temperature during the tests increased, mostly in the room with the largest number of people. The influence of the number of people on the correlation between the measured parameters has not been determined. It is also not specified if external conditions influence this correlation. Humidity measurements should be continued in rooms where there are a lot of people in various aspects. Above all, there must be constructed the mechanisms to control it.

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“…This can have a significant influence on indoor temperatures, building energy dynamics and power requirements, as well as occupant comfort. Today, in response to the growing worldwide interest in energy dynamics, building designers, many research institutions, government agencies, and energy companies are rediscovering the principles of building thermal response [1][2][3][4][5] and passive solar architecture [6,7] which have been known for half a century [8,9]. This is in addition to a variety of more or less advanced building energy efficiency strategies based on thermal mass [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Application of Phase Change Materials and Conventional Thermal Mass for Control of Roof-Generated Cooling Loads

Kośny

Miller

Yarbrough³

et al. 2020

Applied Sciences

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Among all of the internal fabric and external enclosure components of buildings, sloped roofs and adjacent attics are often the most dynamic areas. Roofs are exposed to high temperature fluctuations and intense solar radiation that are subject to seasonal changes in climatic conditions. Following the currently rising interests in demand-side management, building energy dynamics, and the thermal response characteristics of building components, this paper contains unpublished results from past studies that focused on innovative roof and attic configurations. The authors share unique design strategies that yield significant reduction of daytime roof peak temperatures, thermal-load shavings, and up to a ten-hour shift of the peak load period. Furthermore, advance configurations of the roofs and attics that are discussed in this paper enable over 90% reductions in roof-generated peak-hour cooling loads and sometimes close to 50% reductions in overall roof-generated cooling loads as compared with traditionally constructed roofs with the same or similar levels of thermal insulation. It is expected that the proposed new roof design schemes could support the effective management of dynamic energy demand in future buildings.

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Experimental determination of parameters in models of indoor air temperature response to reduction in supplied energy

Cited by 3 publications

References 19 publications

Estimating time constants for over 10,000 residential buildings in North America: towards a statistical characterization of thermal dynamics

Estimating time constants for over 10,000 residential buildings in North America: towards a statistical characterization of thermal dynamics

Correlation of humidity and temperature in conference rooms - preliminary tests

Application of Phase Change Materials and Conventional Thermal Mass for Control of Roof-Generated Cooling Loads

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