Design of real-time data acquisition and regulation algorithm of air-conditioning equipment for grid supply-demand interaction
Bibliographic record
Abstract
On the basis of ensuring the balance between supply and demand of the power grid, fully realizing the automatic control of the air conditioning system can make the energy consumption of the air conditioning operation reduce significantly, thus realizing the purpose of energy saving.This paper combines a variety of technologies to establish an intelligent air conditioning measurement and control system, realizes terminal communication through the CoAP protocol, and designs the corresponding system hardware as well as the real-time data acquisition method for air conditioning equipment.Based on the PID principle, the temperature and humidity control strategy of air conditioning equipment based on expert PID is proposed.In order to better ensure the energy-saving control efficiency of air-conditioning equipment, this paper fully considers human thermal comfort and the interaction between supply and demand of the power grid, establishes a comprehensive optimization control model with the objectives of user power consumption and human comfort, and passes through the PSO algorithm in order to obtain the optimal control results.Simulation found that when the initial temperature is lower than the set value, the expert PID control strategy will adaptively realize the air conditioning temperature and humidity adaptive regulation to ensure that the indoor temperature is within a reasonable range.The total power consumption of the grid is reduced by 90.18kW compared with that before optimization, and the maximum value of human comfort evaluation is improved by 11.39%.Relying on the intelligent air conditioning control system, the adaptive control of temperature and humidity can be effectively realized and the indoor air quality can be better ensured, and a reliable control strategy can also be provided to ensure the balance between supply and demand of the power grid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".