OPTIMIZATION OF AIR CONDITIONER (AC) ENERGY WITH ANN HYSTERESIS METHOD BASED ON INTERNET OF THINGS (IOT) INTEGRATED WITH SMARTPHONE

• Main Author: Hendry, Joshua
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/55003
• Institution: Institut Teknologi Bandung

One of the technologies that can be empowered for energy efficiency is the Internet of Things (IoT) system. In this study, the energy efficiency that is reviewed is carried out on the air conditioning (AC) system. This study also examines two thermal parameters, namely temperature and humidity. This research has several objectives, namely, to build an IoT-based control system that can streamline AC energy consumption and build a system that can represent the thermal state of the room. The process of sending data is carried out with the MQTT connection protocol. The MQTT protocol is very commonly used as an IoT-based connection protocol. The data collection process is carried out with sensors. The data that has been collected will be stored in the database. The database used is also connected to the IoT system so that data can be presented to users in real-time. The method used for air conditioning control is the hysteresis method. This research also uses the ANN (Artificial Neural Network) algorithm to perform calculations on hysteresis control parameters. In this experiment, the room characteristics are represented by determining the RC value (thermal state of the room). This RC value is measured by fitting it at room temperature. This RC value is also one of the inputs for the ANN model. Not only the ANN algorithm, but users can also perform manual control. In manual control, users can directly input hysteresis parameters without having to use ANN assistance. With the predetermined thermal conditions, this experiment resulted in energy savings of 44% for the ANN input control and 34% for the manual input. With the temperature set at 25°C, the temperature difference obtained is relatively small, 0.39oC for ANN input and 0.26°C for manual input. All these controls are integrated with a smartphone in real-time, so that users can carry out the control process on the smartphone.