A numerical comparison of the impact of different climatic conditions in different geographic locations on the construction of an office building
Bibliographic record
Abstract
The aim of my research is to observe the variance of energy efficiency of a typical multi-story office building under the exposure of different climatic conditions. Energy efficiency requirements in building codes or energy standards are among the most important single measures for buildings’ energy efficiency. Therefore, this study can be set up for a better understanding of how energy efficiency of a building changes under the effect of adverse to moderate climatic conditions which possess a mentionable effect on the operation of a building. This thesis is structured in three balanced and conceptual steps. Following the aim of the project, the virtual building model is to be analyzed under the effect of seven distinct climatic conditions namely work environment of New Delhi, Mumbai, Berlin, Lisbon, Copenhagen, Dubai and Montreal. Firstly, the task is to do a complete literature research based on the scope of similar researches and studying the problems in detail along with the theoritical background all the concepts which are implemented to get the numerical results. This chapter also comprises a detailed study of the climatic conditions of the above-mentioned cities. Different climatic traits like temperature variations, count of heating and cooling degree days, relative humidity, temperature range and comfort zonal charts for the specified cities are studied in detail. This study helps to understand the effect of these adverse to moderate climates on the operation of the building. On the second step, the virtual building model is prepared on a software platform named Revit Structures. This virtual building model is not necessarily a complete building, but it has the relevant functionalities of a real building. We perform the energy analysis and the heating and cooling analysis on this virtual building model to study the operational outcome of the building under different climatic conditions in detail. By the end of these above two tasks, two scenarios are observed. On one hand, we have a literature research and on the other hand we have the numerical results. Therefore, finally we present a comparative scenario based on the energy efficient performances of the building under such variant climatic conditions. This is followed by the prediction of thermal comfort level inside the building and it based on Fanger’s PMV Model. Understanding the literature and the numerical values in detail helps us to predict the index thermal comfort level inside the building. The conclusion of this master thesis focuses mainly on the scopes of improvement of energy efficiency requirements in energy codes if any, differentiated according to specific locations. The initial aim of my hypothesis which is to study the impacts of climatic variations on the energy efficient performances of a building is fulfilled but as such topics have very deep and broad roots, the scope of further improvements is always predominant.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".