Temperature and Humidity Prediction at Telkom University Landmark Tower (TULT) Using Neural Networks and the Internet of Things (IoT)

TULT is a bustling hub for teaching and learning activities, catering to a daily population of at least 1000 students. The comfort of the learning environment is directly influenced by the air conditioning system on each floor. Consequently, there is a pressing need for a method to continuously monitor temperature and humidity levels within the Telkom University Landmark Tower (TULT) building, given that each floor's unique height contributes to variations in these parameters. The diverse altitude-related conditions make accurate temperature and humidity prediction challenging. To address this challenge, we propose harnessing the power of neural networks and IoT methods to predict temperature and humidity at different heights within TULT buildings. The Artificial Neural Network (ANN) serves as an information processing system, emulating the cognitive functions of the human brain and making informed decisions based on previously studied data. Complementing this, the Internet of Things (IoT) represents the integration of the internet, mobile computing, and seamless connectivity into our everyday lives. IoT embodies a shift from the traditional Internet of People to the Internet of Machine-to-Machine (M2M) interactions, often referred to as the “Disruption of Things” (DoT). By integrating ANN with IoT, our approach demonstrates a remarkable temperature prediction accuracy, achieving an impressive Root Mean Squared Error (RMSE) value of 0.963. This advanced predictive capability holds significant promise in optimizing the learning environment's comfort and ensuring a conducive atmosphere for both students and educators.