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Towards sustainable energy system options for improving energy access in Southern Africa

2020· article· en· W3037032170 on OpenAlexaff
Henerica Tazvinga, Oliver Dzobo, Maxwell Mapako

Bibliographic record

VenueJournal of Energy in Southern Africa · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsCanadian Society of Intestinal Research
FundersUniversity of JohannesburgNational Research Foundation
KeywordsRenewable energyEnvironmental economicsSoftware deploymentLivelihoodBusinessElectricityEnergy povertyDistributed generationFossil fuelComputer scienceEngineeringAgricultureEconomics

Abstract

fetched live from OpenAlex

Access to modern energy services is one of the pre-requisites to improved livelihood, yet the poor, particularly in developing countries, remain tied to unhealthy and inefficient traditional fuels. Renewable energy technologies are increasingly popular energy supply alternatives to fossil-based fuels in many countries. This study presents sustainable energy system implementation options for increasing energy access in developing countries, with special emphasis on Sub-Saharan Africa. A feasibility case study and various implementation options are presented for possible deployment of these systems. Hybrid optimization of multiple energy resources software was used to simulate and validate the proposed hybrid system design and performance. The simulation results indicate that hybrid systems would be feasible options for distributed generation of electric power for remote locations and areas not connected to the electricity grid. Such a hybrid energy system, through providing modern energy services, gives promise to free-up rural communities to engage in productive activities. The opportunity to power or facilitate productive activities such as agro-processing, fabrication and services can potentially reduce poverty.

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 categoriesMeta-epidemiology (narrow)
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 score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.224
Teacher spread0.204 · 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.

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

Citations12
Published2020
Admission routes1
Has abstractyes

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