An autonomous system via fuzzy logic for residential peak load management in smart grids
Bibliographic record
Abstract
Residential Heating, Ventilation, and Air Conditioning (HVAC) systems can play significant role in the future smart grids in order to balance demand and supply patterns as they are the main electrical load during peak load periods. Programmable thermostats and programmable communicating thermostats are widely used for automatic control of residential HVAC systems with the aim of energy management and providing thermal comfort while users set their daily/weekly schedules and preferences. On the other hand, the programs such as Time-of-Use (TOU) rates, Real-time Pricing (RTP), and Demand Response (DR) are often applied by utilities in order to encourage users to reduce their consumption during peak load periods. However, it is often an inconvenience for residential users to manually modify their schedules and preferences based on the electricity prices that vary over time. Hence, in this paper an autonomous thermostat capable of responding to different parameters such as time-varying prices, while saving energy and maintaining user's thermal comfort is presented. The developed thermostat is the result of integration of fuzzy logic, wireless sensors, and smart grid initiatives. To implement and validate the approach; a house simulator that represents a smart thermostat is developed in Matlab-GUI. The simulation results demonstrate the overall improvement with respect to energy saving and conservation without jeopardizing occupant's thermal comfort.
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 | 0.001 | 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".