Smart Rooftop Cooling and Ventilation System

Abstract

The heat that is gathered by the roofs of the residential buildings in tropical areas like Malaysia is usually overwhelming which results in extreme thermal discomfort indoors. Traditional cooling methods, including air conditioning and passive ventilation are often inefficient in energy consumption and are not flexible enough to react well to the dynamic climatic conditions. To solve this problem, the paper presents an Artificial Intelligence (AI) Smart Rooftop Cooling and Ventilation System to optimize the airflow and temperature regulation in the house by predictively automating these processes. The key point is to develop a smart system with the help of an ESP32 microcontroller, exhaust fans, mist sprayers, and environmental sensors, combined with a machine learning model, to predict the temperature trends and activate time-based cooling. The strategy uses a hybrid control system involving real-time sensor feed with AI predictions on the basis of a Multiple Linear Regression (MLR) model. The findings indicate that the system will effectively cool rooftops and adjust ambient temperatures to 36˚C and above in a few minutes. Besides, the system has a smart energy control where the fans are slowed down to the required temperature and has a rain detection safety interrupt. To sum up, the system manages to combine embedded engineering, AI, and IoT in the creation of a relatively inexpensive sustainable system to control the thermal comfort of residences.