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Sustainable Electricity Generation in Developing Countries: Case Study in a Sub-Saharan Rural Community

2022· article· en· W4319430811 on OpenAlexaff
Cristian Giovanni Colombo, Michela Longo, Wahiba Yaïci, Dario Zaninelli, Marco Pasetti

Bibliographic record

Venue2022 IEEE Sustainable Power and Energy Conference (iSPEC) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsElectrificationElectricityRural electrificationRenewable energyDeveloping countryElectricity generationEnvironmental economicsWork (physics)BusinessNatural resource economicsMains electricityElectricity retailingSustainable developmentEconomic growthElectricity marketEconomicsEngineeringPolitical sciencePower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

With the current global trend of decarbonization and electrification, several countries launch their campaign of ecological transition. Electricity generation sector still represent the main sources of CO<inf>2</inf> emission, and several countries still afford only on fossil fuels. Developing countries represent a significant percentage of them. In addition, the access to electricity to many communities, especially in rural areas, still represent a major issue for the development of these countries. The implementation of a micro-grid powered by renewables, highly available in these areas can represent the key to overcome the barrier of electricity access. Based on these considerations, this work wants to simulate the possibility to provide clean electricity, generated by a PV plant, to a small community, composed by 500 families, in the rural areas of Sub-Saharan Africa. The case study also highlights the possibility to supply easily the load in an economical sustainable way, generating electricity at an affordable price, in order to make more accessible electricity also to the poorest communities.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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