Sensitizing performance of air purifiers for the high-rise commercial buildings in urban core
Bibliographic record
Abstract
There are thousands of pollution monitoring stations which are recording the data 24×7, the present research question is using this data to solve bring out a relationship between natural ventilation and air conditioning. Recently, WHO reported that 14 out of the top 15 most polluted cities are in India. Every year there is a loss of 6.2% to the global economy due to air pollution. The recent urban PM2.5 smog spread over the whole of north India covering about 50% of the country’s population. This event has been increasing the use of air purifiers and affecting the building energy performance. Most air purifiers (PM 10 and PM 2.5) are energy-intensive but are not always equipped with sensors. In commercial buildings, air purifiers are operated based on publicly relayed pollution information. The air pollutants that infiltrate into buildings are based on leaks, cracks, quality of building construction and pressure differences. Since indoor pollution levels are less than outdoor pollution levels, usage of air purifiers based on outdoor information leads to overperformance and hence energy wastage. Therefore, there is a need for optimization in sensitizing the performance of air purifiers at the building level. This study intends to assess the role of building airtightness and air purifier automation in lessening the air purifiers’ electricity consumption in urban areas. Transient building simulation tools do not account for infiltrated pollution levels directly. Virtually evaluating the energy savings through air purifier automation and the building’s airtightness would not be a straightforward assessment. The following paper uses EnergyPlus Energy Management System Class along with air pollution data monitored to model and simulate the Business-as-usual (BAU) and proposed Automation scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".