Indoor Temperature Characterization and its Implication on Power Consumption in a Campus Building
Bibliographic record
Abstract
Building monitoring and management are some of the important components of smart cities. It provides valuable information to the city manager and power supplier to better optimize their resources. With a steady rise in electricity prices in recent years, the importance of efficient use of the Heating, Ventilating, and Air-Conditioning (HVAC) systems becomes vital since they contribute to more than 10% of building power consumption. Given the growth on the Internet of Things (IoT) more HVAC equipment is being deployed with sensors. These sensors can produce large amounts of data that can be transformed into knowledge about the operation of a building. In this paper, we examine a large amount of sensor data from a building with more than 200 rooms. We analyze the power consumption of the building and compare different algorithms to predict the power consumption of the building using indoor and outdoor temperatures. We compare 8 different Machine Learning (ML) algorithms in order to examine their effectiveness. We then cluster rooms based on the temperature settings. Our evaluation results illustrate reasonable prediction accuracy and pinpoint several clusters with an inefficient temperature setting. The results can help the university to better utilize its resources and reduce the power consumption costs.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".