Design of a DC Microgrid System for a Remote Community in Nigeria
Bibliographic record
Abstract
This paper presents the design of a DC microgrid for a remote community in Edo State, Nigeria having a solar irradiance of 4.63 kWh/m2/day. The community is isolated and located far away from the city with no access to the electricity grid. There is a need for lighting and running of electronics, as the main source of lighting presently is kerosine, which is not efficient and leads to health issues. The community is made up of 9 residences that are not more than 100 m apart. House 1 was selected as the standard house with a load of 1 kWh/day, while the other 8 houses have a load difference of ±10% with reference to house 1. Using a 48 V DC bus, the designed PV system components comprise of a 100W solar photovoltaic (PV) panel and a 12 V 45 A·hr battery. The system was sized using Homer Pro. Optimization results presented various design for the various houses. The result obtained showed reasonable and feasible cost-effective solution in terms of the Net Present Cost in both installation and running of the hybrid system for the community. Sensitivity analysis was also carried out to test the adaptability of the system using a solar irradiation input of ±10%. Detailed result of the analysis is presented in the paper.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".