Abstract
Hydroponics systems require a Thermostat to regulate the water temperature. Environmental conditions affect changes in water temperature, so it requires analysis of environmental variables such as air temperature and humidity, light intensity, and the temperature of the water itself. The research developed a solar-powered intelligent thermostat system using the Fuzzy Rule-Based Regression (FRBR) algorithm and the Internet of Things. The FRBR algorithm is used to regress the use of electrical energy used for thermostat purposes and hot and cold-water pumps produced by ceramic element heaters. The IoT system consists of two-element ceramic heaters and a pump that is controlled using a relay module triggered by a water temperature sensor. The environmental sensors used are air temperature and humidity sensors and light intensity as precedents of the rule. While dependent consists of temperature, Total Dissolved Solids (TDS) in water and energy consumption. The test was carried out using a hydroponics system wick with three air conditions, namely sunny, overcast, and rainy. The association rule mining evaluation showed that FRBR produced 12 main rules with an average support of 0.424 and confidence of 0.824. Regression results showed a mean square error (MSE) of 0.339 for water temperature and TDS and 0.141 for electrical energy consumption. The results showed that the system built did not change the TDS value in water so in the future, comparison of TDS values will be carried out without and with the thermostat system.
Concepts :
Citations by Year
| Year | Count |
|---|---|
| 2025 | 0 |