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Record W2807770285 · doi:10.29173/mocs161

Energy Performance Monitoring and Analysis of NetZero Energy Homes (NZEHs)

2015· article· en· W2807770285 on OpenAlexaffvenueabout
Hong Li, Haitao Yu, Mustafa Gül, Mohamed Al‐Hussein, Ahmad Saleh Al-Rifai, Li Yang

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnergy consumptionEnergy (signal processing)Consumption (sociology)Energy balanceEfficient energy useTotal energyEnvironmental economicsArchitectural engineeringEnvironmental scienceEnergy accountingEngineeringElectrical engineeringStatisticsEconomics

Abstract

fetched live from OpenAlex

Residential building operations consume a considerable amount of energy, especially in coldclimate regions. The National Energy Board of Canada (NEB) analyzed energy consumption in 2011, and found that energy use in the residential sector, including space heating/cooling, hot water heating, lighting, appliances, and other energy-using devices, accounts for 14% of the total energy consumption nationally. The concept of NetZero-energy homes (NZEHs) has emerged as a solution to reduce the energy demands of residential building operations. Following efforts to develop NZEHs, the actual energy performance of these homes needs to be examined, and sensor technology is capable of measuring this energy consumption in detail. In this research, sensor instrumentation is customized for NZEH projects developed by Landmark Group of Builders in Edmonton, Canada. Data is collected for the first month and following winter months, then the collected data is validated and cleaned and is analyzed in terms of energy consumption, energy generation, and energy balance. Based on the analysis, recommendations for the operation of NZEHs are proposed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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 teacher head, 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
Published2015
Admission routes3
Has abstractyes

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