An evaluation of the suitability of lumped-capacitance models in calculating energy needs and thermal behaviour of buildings

Vivian, Jacopo; Zarrella, Angelo; Emmi, Giuseppe; Carli, Michele De

doi:10.1016/j.enbuild.2017.06.021

Cited by 79 publications

(36 citation statements)

References 20 publications

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“…The second step is the determination of a time constant for each identified analysis period by treating the thermal space of every home as a first-order resistance-capacitance (RC) model (Vivian et al, 2017). While this is a relatively simple model, it provides a useful characterization of the dynamic behaviour of the house.…”

Section: Building Time Constant Estimationmentioning

confidence: 99%

“…This unprecedented level of access to raw data will promote the potential of machine learning, visual analytics, and data-driven statistical modelling techniques in estimating the building's dynamic thermal response (Miller, Nagy, & Schlueter, 2017). Grey-box model approaches use operational data from numerous real buildings to calibrate a simplified physical model, in the effort of providing good approximation of a building's thermal response (Vivian, Zarrella, Emmi, & De Carli, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The estimation is based on temperature measurements and equipment runtimes acquired from smart thermostats. When limited information is available, the knowledge provided in this paper can: i) assist in the generation of simple grey-box models that could guide the preliminary design of new residential buildings (Vivian et al, 2017), and ii) assist in the development and adoption of effective load management strategies for existing buildings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Building Time Constant Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…To confirm that the dominant time constant of the building increased when implementing the ITC/TSM system, a free response simulation was conducted. Equation (6) was used to model the response of each case.…”

Section: Verification Of Building Thermal Time Constant Incrementmentioning

confidence: 99%

“…Other research using building thermal mass for energy reduction have been well documented [6][7][8][9][10][11][12][13]. The thermal inertial of a building is one of the controlling factors for the phase shift and amplitude reduction of "heat flow fluctuations" associated with the building's internal temperature response due to weather [11].…”

Section: Introductionmentioning

confidence: 99%

Building Energy Management Using Increased Thermal Capacitance and Thermal Storage Management

et al. 2018

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This study simulates an increased thermal capacitance (ITC) and thermal storage management (TSM) system to reduce the energy consumed by air conditioning and heating systems. The ITC/TSM is coupled with phase change materials (PCM), which enable tank volume reduction. The transient energy modeling software, the Transient System Simulation Tool (TRNSYS), is used to simulate the buildings' thermal response and energy consumption, as well as the ITC/TSM system and controls. Four temperature-controlled operating regimes are used for the tank: building shell circulation, heat exchanger circulation, solar panel circulation, and storage. This study also explores possible energy-saving benefits from tank volume reduction such as losses associated with the environment temperature due to tank location. Three different tank locations are considered in this paper: outdoor, buried, and indoor. The smallest tank size (five gallons) is used for indoor placement, while the large tank (50 gallons) is used either for outdoor placement or buried at a depth of 1 m. Results for Atlanta, Georgia show an average 48% required energy decrease for cold months (October-April) and a 3% decrease for warm months (May-September) for the ITC/TSM system with PCM when compared with the reference case. A system with PCM reduces the tank size by 90% while maintaining similar energy savings.

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A grey-box modelling methodology to express home heat-energy usage as statistical distributions — case studies in urban Ireland

Beagon

Boland

2022

Energy Efficiency

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Home energy retrofit has recurred in public policy throughout recent decades. However, the savings in energy usage attributable to home retrofit have remained difficult to accurately predict. Occupants cause prediction inaccuracies by varying different factors, especially heating setpoints temperatures and heating patterns. Acting together, such occupant factors result in distributions — not single values — of heat-energy usage, even among similar homes. Datasets of heat-energy distributions can be found by building performance simulation using modern grey-box models. This study presents a methodology to simulate grey-box models of home heating through ranges of heating setpoints and patterns. An entire process to calibrate, validate and simulate at a large scale is described, and then demonstrated using case studies. Grey-box models, written in Modelica language, can conveniently simulate through large ranges of occupant factors. The case studies exploited this advantage of grey-box models to simulate empirical data on occupant factors. (For instance, empirical data found that home heating setpoints shifted before and after home energy retrofit.) In doing so, the datasets of simulation results enabled the exploration of home heat-energy usage with the normal and Weibull statistical distributions. Additionally, the heat-energy distributions of case-study homes were statistically tested, first for retrofit savings, second for equality to each other and third for equality to an official heat-energy estimate. Results demonstrate that home heat-energy usage, at a large scale, is best expressed as a Weibull distribution not normality. After home energy retrofit, heat-energy usage displays less variation (in general), less skewness, and thus becomes closer to normality. Occupant factors were found to vary home heat-energy usage into distinct distributions, even within similar homes. Therefore, in most case-study homes, heat-energy usage did not equal an official estimate. Finally, shallow retrofit of a modern home in Ireland fails to save heat-energy usage by most occupants.

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An evaluation of the suitability of lumped-capacitance models in calculating energy needs and thermal behaviour of buildings

Cited by 79 publications

References 20 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

Building Energy Management Using Increased Thermal Capacitance and Thermal Storage Management

A grey-box modelling methodology to express home heat-energy usage as statistical distributions — case studies in urban Ireland

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