CEMA: Comfort Control and Energy Management Algorithms for use in Residential Spaces through Wireless Sensor Networks
Bibliographic record
Abstract
In recent years, many strides have been achieved in the area of Wireless Sensor Networks (WSNs), which is leading to constant innovations in the types of applications that WSNs can support. Much advancement has also been achieved in the area of smart homes, enabling its occupants to manually and easily control their utility expenses. In this thesis, both areas of research will be colluded for a simple, yet critical application: efficient and economical comfort control in smart residential spaces. The goal is to design a central, modular energy consumption control system for residential spaces, which manages energy consumption in all aspects of a typical residence. This thesis is concerned with two facets of energy consumption in residences. The first facet is concerned with controlling when the heating, ventilating, and air conditioning unit (HVAC) operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole. The second facet is concerned with controlling the lighting in each room so as to not exceed a certain input value. The communication network that supports the realization of these coveted goals is based on Zigbee interconnected sensor nodes which pour data unto a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management, if needed. A Java-based discrete event simulator is then written up to simulate a floor of a typical Canadian single-family dwelling. The simulation assumes errorless communication and proceeds to record certain room variables and the ongoing cost of operation periodically. These results from the simulator are compared to the results of the well known simulator, created by DesignBuilder, which describes typical home conditions. The conclusion from this analysis is that the Comfort Control and Energy Management Algorithms (CEMA) are feasible, and that their implementation incurs significant monetary savings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".