MétaCan
Menu
Back to cohort
Record W6889092340 · doi:10.25394/pgs.21657803

Extension, Evaluation, and Validation of Load Based Testing for Residential and Commercial HVAC Equipment

2022· dissertation· en· W6889092340 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2022
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHVACEconomizerEfficient energy useAir conditioningUnit testingElectricityCooling loadPopulation

Abstract

fetched live from OpenAlex

With rising temperatures, urbanization, population growth, improving economic wellbeing, decarbonization and electrification efforts, the demand for space cooling and heating equipment is continuously increasing around the world. To counteract the effect of rising demand for air conditioners and heat pumps on total energy consumption, peak electricity demand, and emissions, it is crucial to promote the development and market penetration of energy-efficient systems. Establishing minimum energy performance standards (MEPS), energy labeling and utility programs are some of the effective and tested methods for achieving this goal. The technical basis for these energy efficiency standards is a testing and rating procedure for estimating equipment seasonal performance from laboratory tests. Although the current rating procedures provide standardized metrics to compare different equipment performances, they fail to appropriately characterize the field representative performance of systems by not considering the effects of: 1) test unit embedded controls, thermostat, and realistic interactions with the building load and dynamics; 2) different climate zones and building types; and 3) and other integrated accessories for improving energy efficiency such as economizer for rooftop units (RTUs). Therefore, current approaches for performance ratings neither incentives the development and implementation of improved system and control designs nor consumers with a metric that represents the advanced systems' actual energy savings. To address this, a load-based testing methodology that enables dynamic performance evaluation of equipment with its integrated controls, thermostat, and other accessories was recently proposed. The test methodology is based on the concept of emulating the response of a representative building conditioned by the test unit in a test lab using a virtual building model. In this work, the proposed load-based testing methodology was further extended, evaluated, and validated for residential heat pumps to integrate it into next-generation energy efficiency testing and rating procedures and to serve as a tool for engineers to develop and validate improved control algorithms in a laboratory setting. Further, a load-based testing method for evaluating the dynamic performance of RTUs with integrated economizers was also developed and demonstrated. A load-based testing approach previously developed for residential cooling equipment is extended for heat pump heating-mode and demonstrated for a variable-speed system. The heat pump's typical dynamic behaviors are captured along with controller imperfections that aren't reflected in current testing approaches. Further, a comprehensive comparison was performed between the proposed load-based testing approach to the current steady-state testing approach in the U.S., AHRI 210/240, based on performance evaluation of three residential variable-speed heat pumps to understand the differences and their significance for the next-generation rating procedure. For cooling mode, steady-state testing estimates higher seasonal performance, but for heating mode, the steady-state testing approach estimates higher seasonal performance for warmer climates and is comparable for colder climates. The load-based testing methodology was validated by comparing the laboratory performance of a heat pump to that of a residential building in a controlled environment. The virtual building modeling approach for building loads and thermal dynamics effectively captured these characteristics of the house. The heat pump's cycling rate response with run-time fraction, which represents the unit's overall dynamic response, matched well between lab load-based tests and house tests. The test unit's COP difference for cooling and heating tests was within 3% between the two facilities, except for 9% in 95°F and 6% in 104°F cooling dry-coil test intervals. To evaluate the applicability of the developed load-based testing methodology as next-generation rating standards, its repeatability and reproducibility were assessed based on multiple heat pump round-robin tests conducted in two labs. Overall, reasonable to good repeatability was observed in load-based test results in both labs, however, poor reproducibility was observed except for one heat pump heating mode results. A root cause analysis of the observed differences along with recommendations for a next-generation rating approach are presented. This work aided in the development of a CSA (Canadian Standards Association) standard EXP07:19 and its subsequent revision for equipment rating based on load-based testing. The application of the load-based testing methodology as a tool for the development and evaluation of a residential heat pump controller design was demonstrated. Further, a load-based testing methodology was developed and demonstrated for the dynamic performance evaluation of RTUs with integrated economizers in a test laboratory setting. Recommendations for future work to further develop and improve the repeatability, reproducibility, and representativeness of the load-based testing and rating approach for residential and commercial air conditioners and heat pumps are summarized at the end of the dissertation.

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)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.227
Teacher spread0.209 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePurdue e-Pubs (Purdue University System)Same topicBuilding Energy and Comfort OptimizationFrench-language works237,207