Generation of Typical Meteorological Year Data for a City in South-Western Nigeria
Bibliographic record
Abstract
Abstract Weather data is essential in determination of the performance of energy systems such as HVAC and solar energy systems. Weather data therefore, can be employed in designing more energy-efficient systems. This work focuses on the generation of a Typical Meteorological Year (TMY) Data for Abeokuta (7°7′1″ N, 3°22′13″E), a state in South-Western Nigeria by the use of a modified Sandia method. It makes use of seven weather parameters obtained over 29 years (1984 - 2012) in the construction of the TMY. Sandia method is highly affected by solar radiation even if its weight is reduced by half. The weights of other parameters such as temperature, wind, and relative humidity have less impact on the selection of TMY. The results of this study show that the weather pattern of Abeokuta follows the general diurnal variation of dry/harmattan season and wet/rainy season, typical to Nigeria. In this work also, the effect of general rise in temperature and solar radiation, indicative of global warming, is highlighted. Higher solar radiation values are majorly typical of a low cloud cover, and high clearness index. Suggestions were also made as to the preferable type of sustainable energy system to make use of in this location, as well as in the development of energy systems and data collection in Nigeria. The relevance of this work is that it would help to strengthen building energy standards, understand the local climate of Abeokuta and hence facilitate building energy system performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".