Forecasting Hydropower Generation in Ghana Using ARIMA Models
Bibliographic record
Abstract
In this study, an Autoregressive Integrated Moving Average (ARIMA) model was used to forecast Ghana’s Akosombo dam level and hydropower generation by the end of year 2022. Data used for this study span from January 2010 to December 2019. Base on the final ARIMA model, power generation is forecasted to decrease from 398 Megawatts/hr in December 2019 to approximately 374 Megawatts/hr by December 2022. On the other hand, water level of the Akosombo dam is predicted to decrease marginally from 264.8 ft in December 2019 to approximately 255.19 ft by December 2022. The Volta River Authority (VRA) and managers of the electricity production in Ghana are encouraged to be proactive in expanding energy production by turning more to renewable energy sources. In the coming years, as they seek to provide sustainable electricity for their cherished customers, investment decisions should be directed towards protecting the volta river from drying up due to human and climatic activities as well as expanding energy mix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".