A Remote Thermostat Control and Temperature Monitoring System of a Single-Family House using openHAB and MQTT
Bibliographic record
Abstract
In this research, an open-source IoT platform named openHAB smart home automation is used as a home server, an ESP32 Thing microcontroller board is used to design a remote-control system for a thermal energy storage system. It consists of temperature sensors for real-time temperature monitoring, ESP32 Thing board is used for data receiving, processing and sending it to the MQTT broker, openHAB software installed in the personal computer is used as a home server for creating dashboard panel, MQTT broker is used to establishing the communication in between openHAB home server and ESP32 Thing board, Wi-Fi router is used to create the communication channel, a battery-powered remote-controlled heater with a digital thermostat is used as a testing device where user can set the desired temperature for house heating. The main objectives of this work are to design a low-cost monitoring and control system for thermal energy storage systems, to monitor the real-time temperature data, to design a control system for thermostat settings with the following features such as manual/automatic operations, local/remote control options. The user can access the dashboards locally via any computer and remotely via openHAB Cloud console from anywhere in the world. The proposed system in this work will help residence to manage their heating systems smartly in a cost-effective way, which will be the replacement of the conventional thermostat settings. The utility provider company can also use this system to control the thermostat settings from centrally, wirelessly, and remotely.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".