A fuzzy logic system for demand-side load management in residential buildings
Bibliographic record
Abstract
Participating in demand response has significant advantages for both consumers and electricity producers, i.e., saving on high electricity prices for the user, and helping utilities in peak load curtailment. In this paper, a new concept based on fuzzy logic for demand-side load management in residential buildings is presented. The proposed fuzzy logic rule-based algorithm is developed for four important factors: current outdoor temperature, current load demand, current electricity price, and occupant presence. The approach proposed is the result of integration of wireless sensors and fuzzy logic concept. The approach is applied to existing wireless programmable thermostats to add more intelligence for load reduction in residential buildings by considering the input parameters. To gain better understanding, the wireless programmable thermostat is designed and simulated in Matlab GUI as a simulator. The results show that the system by combining the fuzzy rules reduces the temperature (load reduction) which results in better energy management and conservation in heating/cooling systems. Besides, the method is automatically able to help households to decrease their load during peak price and peak load periods. By considering the user activities in the house, it provides occupant's thermal comfort as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".