Elevating Efficiency: A Technological Framework for Smart City Initiatives in High-Rise Residential Towers of Addis Ababa, 2013 Context
Bibliographic record
Abstract
This study focuses on the implementation of smart city initiatives within high-rise residential towers in Addis Ababa, Ethiopia, with a particular emphasis on reducing energy consumption. A mixed-methods approach was employed, integrating surveys with sensor data analysis to assess the impact of smart city technologies on energy consumption patterns. Statistical models were used to predict future trends in energy use based on current data. Initial findings suggest that a 15% reduction in electricity usage can be achieved through the implementation of smart lighting and HVAC systems, with a significant increase in user satisfaction noted across all surveyed households (n=200). The technological framework demonstrates potential for reducing energy consumption while improving user experience. Recommendations are provided to further refine and scale these technologies. Further research is recommended to validate the findings through longitudinal studies, and policy recommendations should prioritise funding for smart city infrastructure in high-rise residential areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".