Obtaining the heat loss coefficient of a dwelling using its heating system (integrated coheating)

Farmer, David; Johnston, David; Miles-Shenton, D

doi:10.1016/j.enbuild.2016.02.013

Cited by 32 publications

(15 citation statements)

References 12 publications

(14 reference statements)

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“…Any input that did not directly contribute to the internal temperature change is accounted for in Table 2 under "Power Absorbed by Storage Tanks". Related work shows good agreement between heat loss coefficients obtained from co-heating tests conducted with electric heaters and with a building's own heating system [17], thus justifying the approach in this article.…”

Section: Physical Dynamic Heating Test Experiments and Resultssupporting

confidence: 61%

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

Jankovic

2019

Energies

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Buildings contribute to nearly 30% of global carbon dioxide emissions, making a significant impact on climate change. Despite advanced design methods, such as those based on dynamic simulation tools, a significant discrepancy exists between designed and actual performance. This so-called performance gap occurs as a result of many factors, including the discrepancies between theoretical properties of building materials and properties of the same materials in buildings in use, reflected in the physics properties of the entire building. There are several different ways in which building physics properties and the underlying properties of materials can be established: a co-heating test, which measures the overall heat loss coefficient of the building; a dynamic heating test, which, in addition to the overall heat loss coefficient, also measures the effective thermal capacitance and the time constant of the building; and a simulation of the dynamic heating test with a calibrated simulation model, which establishes the same three properties in a non-disruptive way in comparison with the actual physical tests. This article introduces a method of measuring building physics properties through actual and simulated dynamic heating tests. It gives insights into the properties of building materials in use and it documents significant discrepancies between theoretical and measured properties. It introduces a quality assurance method for building construction and retrofit projects, and it explains the application of results on energy efficiency improvements in building design and control. It calls for re-examination of material properties data and for increased safety margins in order to make significant improvements in building energy efficiency.

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Section: Physical Dynamic Heating Test Experiments and Resultssupporting

confidence: 61%

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

Jankovic

2019

Energies

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“…Recent work has looked to develop the application of dynamic test sequences and analysis (e.g., ARX, ARMAX, state space models) from single components to whole building characterisation [47,48]. Alternatively, Farmer et al [49] and Jack [50] have looked to reduce the intrusiveness of the co-heating method through utilising the existing heating system, with the latter also using occupied dwellings. This less intrusive test, although with some degree of accuracy sacrificed, may then be more applicable to higher levels of deployment.…”

Section: Alternative Methodsmentioning

confidence: 99%

Assessing the Relationship between Measurement Length and Accuracy within Steady State Co-Heating Tests

2017

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Evidence of a fabric performance gap has underlined the need for measurements of in situ building performance. Steady state co-heating tests have been used since the 1980s to measure whole building heat transfer coefficients, but are often cited as impractical due to their 2-4 week test duration and limited testing season. Despite this, the required conditions for testing and test duration have never been fully assessed. Analysis of field tests show that in 12 of 16 cases, a heat loss estimate to within 10% of the result achieved across a full test period can be achieved within just 72 h. These results are supported by simulated tests upon a wider range of dwellings and across wider environmental conditions. However, systematic errors may still exist, even in cases of convergence and cases with significant uncertainties may never converge. Simulated examples of traditional dwellings and those built in line with current building regulation limits may be tested for more than half the year. However, even when simulated with reduced uncertainties, dwellings with low heat loss and high solar gains, such Passivhaus dwellings and apartments, could be successfully tested for just 22% and 12% of a year respectively, demonstrating the limitations of the co-heating method in assessing such dwellings.

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“…The uncertainties associated with occupant behavior when estimating the HLC in situ can be removed by the physical measurement of an unoccupied dwelling [29]. In this research, these uncertainties were completely avoided since the experiment was done in laboratory conditions.…”

Section: Co-heating Test and The Design Total Ventilation Andmentioning

confidence: 99%

“…Following an initial period during which the building fabric reaches thermal capacitance, a Co-heating test assumes the following whole building energy balance [29]:…”

Section: Co-heating Test and The Design Total Ventilation Andmentioning

confidence: 99%

“…where Q is the total measured power input from space heating (W), R is the solar aperture of the building (m 2 ), S is the solar irradiance (W/m 2 ), ΣU×A is the total fabric transmission heat loss (W), C v is the background ventilation heat loss (W), and ΔT is the temperature difference between the internal and external environment [29]. The whole house energy balance equation can be rearranged to show HLC [29]:…”

Section: Co-heating Test and The Design Total Ventilation Andmentioning

confidence: 99%

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Co-heating test as a tool for reduction of energy performance gap in buildings

Krstić¹,

Domazetović²

2020

Int. J. EQ

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This paper presents part of the results of a large-scale, long-term experimental research conducted at the Faculty of Civil Engineering and Architecture Osijek. Among other research goals, this research aims at further development and improvement of a relatively new method used for the measurement of thermal transmittance of walls (U-value) in literature, often called temperature-based method (TBM). This research also partially overlaps with other researches carried out at the Faculty of Economics in Osijek, where the main research goals were development of machine learning and neural network models for predicting energy consumption in buildings, which will reduce the energy performance gap between design and actual energy needs. Building thermal performance as a whole can be quantified by the heat loss coefficient (HLC) or the total heat loss (THL). Experimental research presented in this paper was conducted by using a built test chamber in a laboratory, and the research lasted for 40 days. This is an innovative element of this research, since the test chamber is built inside a laboratory where external weather conditions are simulated by omitting the negative influence of wind, precipitation, and solar radiation on the experimental results. The actual heating energy consumption by the test chamber was recorded daily for 40 days during the winter season, together with internal and external temperatures, relative humidity (RH), U-values of walls, and wind speed. Chamber airtightness was measured at the beginning of the experiment. These measurements made it possible to perform the Co-heating test. This test is used to calculate the total heat loss of a building, both fabric and ventilation loss. Parallel with the Co-heating test, the design heating energy need of the test chamber was determined by calculating the heat loss coefficient and the total heat loss. Actual and design values of heat loss coefficient and total heat loss were used to characterize the energy performance gap. Energy performance gap in this study was found to be between −40% and 13%. Research results indicate the variables affecting the actual and design values of heat losses significantly. Presented results provide guidance for more accurate determination of actual energy consumption in buildings, and therefore help in the reduction of the energy performance gap.

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Obtaining the heat loss coefficient of a dwelling using its heating system (integrated coheating)

Cited by 32 publications

References 12 publications

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

Assessing the Relationship between Measurement Length and Accuracy within Steady State Co-Heating Tests

Co-heating test as a tool for reduction of energy performance gap in buildings

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