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Proof of Concept of a Cloud-Based Smart Dual-Fuel Switching System to Control the Operation of a Hybrid Residential HVAC System

2019· article· en· W3006093126 on OpenAlexaff
Danilo Yu, King Yeung Tung, Navid Ekrami, Gulsun Demirezen, Alan S. Fung, Farah Mohammadi, Kaamran Raahemifar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsHVACThermostatAutomotive engineeringComputer scienceCloud computingSmart gridDual (grammatical number)Demand responseControl systemEmbedded systemAir conditioningReal-time computingEngineeringElectrical engineeringOperating systemElectricity

Abstract

fetched live from OpenAlex

A net-zero energy house was outfitted with IoT technology and devices to monitor its energy generation and consumption. The house has a rooftop PV array, an efficient hybrid (or dual-fuel) HVAC system, and efficient appliances to achieve net-zero status. Signals between the thermostat and the HVAC system were intercepted by a custom-built wifi-enabled circuit board to transmit the operating status of the HVAC unit and receive control signals from a remote server for optimal supervisory control. The project was developed as a proof of concept to show that a cloud-based intelligent control system that automatically chooses the best fuel source to use can benefit the homeowner (19% energy cost savings is possible) and contribute to decarbonization (29% reduction). If employed on a wide scale with a smart grid that sends pricing and demand response signals, huge potentials in reducing carbon emissions can be achieved without sacrificing homeowner costs and comfort.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.183
Teacher spread0.179 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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