Electrification Planner for Ghana Using Open-Source Web GIS
Bibliographic record
Abstract
Energy system planning provides information, such as electrification rate and access, essential to match demand and supply of energy. As countries strive to rapidly grow their economies and increase the living standards of its citizens, energy planning system is essential to keep track of assets, accessibility, adequacy, availability, and distribution of energy resources across locations targeted for development. In Ghana, the Ghana Energy Development and Access Project (GEDAP) was mandated to provide up to 100 % electrification rate to the citizens by 2020. While this time has elapsed, public information system showing the spatial distribution and statistical analysis of electrification rate and access in communities and local administrative areas remain scanty. Such decision support system, which can inform energy investment decisions and policy formulation by local and international investors is not readily available, impeding on the Government of Ghana’s (GOG) electrification expansion efforts. It also hinders the nation in attaining the United Nation’s (UN) Sustainable Development Goal (SDG) 7. Thus, the aim of this study was to develop a decision support system on electrification rate in Ghana. The study used energy access data and open-sourced Geographical Information System (GIS) to map the spatial distribution and provide statistical analysis of electrification rate in the country. The resulting information was connected to a WebGIS that can provide access to query, manipulate, and visualize electrification rate in the counting. The developed system estimated that, presently, Ghana has an electrification rate of 85.16 % as of November 2020. This information, and the system in general, will aid decision makers to make swift decision and provide geospatial evidence-based report in achieving 100 % electrification rate in the country.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".