MétaCan
Menu
Back to cohort

DESIGN OF A LOW-COST HYBRID SYSTEM FOR A CANADIAN HOME: A CASE STUDY

2022· article· en· W4297919876 on OpenAlexfundaboutno aff
E Harikrishnan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsPhotovoltaic systemCharge controllerWind powerElectricityElectrical engineeringRenewable energyBattery (electricity)Power (physics)Alternating currentSolar energyController (irrigation)Energy (signal processing)Automotive engineeringEnvironmental economicsComputer scienceEnvironmental scienceEngineeringVoltageEconomicsPhysics

Abstract

fetched live from OpenAlex

The energy challenge has become the most critical issue in this century, leading to many global conflicts. Solar power is broadly acknowledged as a green technology. A stable and non-outages grid with the lowest cost becomes the target for many people interested in energy, especially after a sharp rise in the regular price of energy. This academic research aims to design a low-cost system for a Canadian home to significantly reduce the power bill of a home in St. Johns, Canada. A house or a small industrial facility can generate enough energy to meet its needs by combining two or three energy sources. In St. John's, Newfoundland, the average yearly wind speed is 6.7 m/s, and monthly average solar radiation exceeds 220 W/m2 . A typical R2000-compliant home uses 68.4kWh per day on average. Many distant dwellings can benefit from hybrid energy systems to generate energy. Photovoltaic and wind power generates electricity by combining solar cells with wind turbines, then integrated into a battery bank. The energy is transferred to an inverter, which produces an alternating current. Before using such energy, significant concerns, such as the input-output relationship, must be considered. A charge controller is required to monitor variations in the energy sources, and a power inverter is needed to convert electric current to alternating current. The economic and geological feasibility is conducted for this case study

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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 designCase report
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 routes2
Has abstractyes

Explore more

Same topicHybrid Renewable Energy SystemsFrench-language works237,207