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Record W2988861968

Integration, evaluation and modeling of thermal comfort in energy efficiency measures : comparing electric heating systems

2019· article· en· W2988861968 on OpenAlexfundno aff
Jérémie Léger

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnergy consumptionThermal comfortWork (physics)ThermalElectric energy consumptionElectric heatingEnergy (signal processing)Measure (data warehouse)Thermal energyMechanical engineeringEfficient energy useAutomotive engineeringSimulationNuclear engineeringComputer scienceEngineeringElectric energyThermodynamicsElectrical engineeringMathematicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Electric heating systems do not to perform all equally in terms of energy consumption. In fact, by changing the heat distribution, thermal comfort can be achieved with less energy consumed. In this thesis, the optimal heat distributions and the heat distributions of electric heating devices are investigated and compared. In the first part of this work, the design, construction, control and validation of a climatic chamber is presented. This experimental tool is essential to compare electric heaters at equal thermal comfort. In what follows, a novel method of investigating the optimal heat distribution numerically is presented. In this method, the concept of virtual heaters is introduced. Virtual heaters are a set of two heat distributions: one that maximizes the total heat loss of a room, while maintaining thermal comfort inside this room; whereas the other minimizes the total heat loss, while still maintaining the same thermal comfort. Using the virtual heaters energy consumption, three new performance indices are introduced. The first performance index measures the effectiveness of a heater to distribute heat; the second measure the significance of the heat distribution inside a room from an energy consumption standpoint; the third measure how the difference in energy from the virtual heater and a real heater. The minimum energy loss can also be used as a measure of the room’s energy efficiency, while the maximum virtual heater gives an indication on heat distributions to avoid. Expanding the investigation on heat distribution, the bi-climatic chamber tool is then used to investigate the temperature distribution and energy consumption of three electric heating systems. The results from this experiment show that not all electric heating systems distribute heat in the same way, and from this fact, they do not all have the same energy consumption when providing similar thermal comfort. The convection heater experimentally tested here outperformed the radiant heater and baseboard heater. The experimental heat distribution results are also compared with those of the virtual heater. Both methods agree that avoiding to heating the windows is most efficient. This comparison also serves, in part, as a validation of the method used to find virtual heaters. Other validations for key calculations in the virtual heater models include: comparing tabulated results to calculated results for the thermal comfort model; and comparing simplified solutions calculated analytically by hand to the one calculated by the model for the heat transfer model. Finally, the virtual heaters are used to investigate how optimal heat distributions change with respect to the room geometry and insulation parameters. Investigated parameters were varied individually to quantify their effects on the energy consumption, the heat distribution, and the sensibility of the room heat loss to heat distribution. Interestingly, the window size, the window insulation level and the air infiltration/exfiltration rate can drastically change the minimum energy consumption heat distribution. It was observed that when increasing each of these three parameters, optimal heat distribution changed from heating the air volume to floor heating. The results also showed that most geometric and insulation parameters can influence the sensibility of heat loss to heat distribution. The percentage increase of energy consumption for the maximum virtual heater when compared to the minimum virtual heater was observed to range from 27.4% to 86.0% for the tested cases. The window insulation was found to be the predominant factor influencing the sensibility of heat loss. In summary, this thesis presents a new concept termed virtual heater that is useful in the investigation of indoor heat distribution. Using the virtual heaters and their associated performance indices, the optimal heat distributions for different room geometry and insulation topologies, and the efficiency of some electric heating devices were assessed. Heat distribution can have a significant effect on the energy consumption of heaters and should be considered in building design. Virtual heaters are tools that can undoubtedly help to find more general understandings of optimal indoor heat distribution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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