Design and implementation of a rule-based learning algorithm using Zigbee wireless sensors for energy management
Bibliographic record
Abstract
The capabilities of wireless sensors networks (WSNs) to measure different variables, could significantly improve the limitations of the existing energy management systems. In this paper, we introduce a combination of rule-based techniques and wireless sensors to demonstrate the capabilities of wireless sensors in reducing the electricity consumption without sacrificing thermal comfort that would help utilities in peak load curtailments. The method is applied to existing programmable thermostats (PTs) to add more intelligence to this device for better energy management in residential buildings. The simulation results demonstrate that the proposed rule-based wireless thermostat performs better than the PTs in various aspects, i.e., learning, electric energy conservation, and occupant comfort that could help utilities in peak load curtailment. Moreover, our method is implemented on a typical residential Air Conditioner (AC) by using of X-bee wireless sensor and Arduino Microcontroller. Conducted results show that the combination of WSNs capabilities and the rule-based method reduce the energy consumption by 33.5% compared to the similar existing AC system.
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".