AI-Driven Smart HVAC Management: An IoT and Machine Learning-Based Approach to Energy-Efficient Building Automation

In order to improve building efficiency and sustainability, this paper describes the construction of an AI-Driven Smart Heating, Ventilation, and Air Conditioning (HVAC) Management system. The proposed system collects real-time information on temperature, humidity, and solar intensity by utilizing Internet of Things (IoT) sensors both within and outside the structure. This is done by leveraging the synergy between Internet of Things (IoT) and Machine Learning (ML). In particular, Light Dependent Resistor (LDR) sensors track light intensity, and Digital Humidity and Temperature (DHT) sensors detect temperature and humidity. Using observed light intensity and humidity levels, the gathered data is used to train a supervised machine learning model that uses the Multiple Linear Regression algorithm to forecast interior temperature. Through an analysis of past data and weather forecasts, the system makes proactive recommendations for the best temperature settings to improve user comfort while decreasing energy consumption. The creation of a user-friendly interface for real-time temperature prediction, the integration of environmental sensing data with forecasting weather data, and the construction of an AI-driven climate control system constitute some of the research's major accomplishments. The suggested approach seeks to transform Heating, Ventilation, and Air Conditioning (HVAC) management by providing a cost-effective and environmentally friendly means of enhancing interior climate control in structures.