Investigation and Field Measurements for Demand Side Management Control Technique of Smart Air Conditioners located at Residential, Commercial, and Industrial Sites
Bibliographic record
Abstract
This paper investigates the response and characteristics of the narrowband power line communication (NB-PLC) technique for the effective control of electric appliances such as smart air conditioners (SACs) for demand side management (DSM) services. The expression for temperature sensitivity by examining the influence of atmospheric temperature variations on power consumption profile of all possible types of loads, i.e., residential, commercial, and industrial loads is derived and analyzed. Comprehensive field measurements on these power consumers are carried out in Lahore, Pakistan. The responses of low voltage channels, medium voltage channels, and transformer bridge for a 3–500 kHz NB-PLC frequency range are presented for DSM services. The master control room transmits control commands for the thermostat settings of SACs over power lines, crossing the transformer bridge to reach the SACs of power consumers by using communication protocol smart energy profile 1.0. The comparison of hourly and daily power consumption profiles under evaluation loads, by analyzing typical and variable frequency air conditioners on setting thermostat temperature at 25 °C and 27 °C conventionally and then by using DSM control technique, is analyzed. A prominent reduction in power consumption is found with the implementation of the DSM control technique.
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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 | 0.000 |
| 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) | 0.001 | 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".