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Record W2624700949 · doi:10.1109/icraie.2016.7939487

Analysis of hybrid energy system for supply residential electrical load by HOMER and RETScreen: A case in Rajasthan, India

2016· article· en· W2624700949 on OpenAlexaboutno aff
Norat Mal Swarnkar, Lata Gidwani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPayback periodNet present valueRenewable energyDiesel generatorInternal rate of returnHybrid systemEnvironmental scienceEngineeringTRNSYSElectrical loadAutomotive engineeringDiesel fuelElectrical engineeringEnergy (signal processing)Computer scienceVoltage

Abstract

fetched live from OpenAlex

This paper presents optimization of hybrid energy system to supply electrical load of a residential house located at Jahazpur, Bhilwara in East-South region of Rajasthan, India. The electrical load of the house is calculated by energy consumption of various electrical appliances used and supplied by hybrid energy system includes Solar Photo Voltaic (PV), Small Wind Turbine (WT), Battery, and Diesel Generator system. The modeling and optimization of hybrid energy system done using HOMER (Hybrid Optimization Model for Electric Renewable) developed by NREL (National Renewable Energy Laboratory). HOMER gives the optimum configuration of hybrid energy system for minimizing the Net Present Cost (NPC) of the system. Optimized system configuration further analysed by using RETScreen software tools developed by ministry of natural resources, Canada. The various analysis done in RETScreen includes Net Present Value (NPV) analysis, payback period analysis, internal rate of return (IRR) analysis, and effect of tax and subsidies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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