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Record W1985992259 · doi:10.1109/epec.2012.6474940

Energy optimization and energy management of home via web services in smart grid

2012· article· en· W1985992259 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceHVACHome automationThermostatSmart gridWeb serviceEnergy consumptionBusiness Process Execution LanguageWireless sensor networkEnergy managementComputer networkEmbedded systemDistributed computingAir conditioningEnergy (signal processing)TelecommunicationsWeb modelingEngineeringWorld Wide WebElectrical engineering

Abstract

fetched live from OpenAlex

The smart home contains a network that connects home elements like sensors, HVAC (heating, ventilation, and air conditioning), appliances and thermostat. In our previous work, we proposed an approach that used web services to remotely interact with smart home elements in a smart grid environment. These interactions include adjusting the temperature or reading energy consumption. We assumed a smart home with a wireless sensor network based on Zigbee. There was a central computer that could access all home elements. Business Process Execution Language (BPEL) was used to implement the web service on central computer. In this paper, we enhance the system by including new features. The algorithm to sell energy back to the grid has been further developed. Furthermore, a function to optimize energy using dynamic programming is added to the system. The performance and advantage of the communications between user and home elements via web services are demonstrated.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.003
GPT teacher head0.163
Teacher spread0.160 · 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

Quick stats

Citations11
Published2012
Admission routes1
Has abstractyes

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