Because of rising electricity usage and environmental concerns, energy efficiency has become a critical necessity in contemporary smart building environments. Conventional building energy management systems may result in wasteful energy use since they frequently rely on manual operation or basic rule-based automation. This study suggests a machine learning-based intelligent energy control system that forecasts the best gadget for preserving home comfort in order to solve this problem. In order to suggest suitable equipment, such as an air conditioner, fan, or window ventilation, the system examines environmental factors like temperature, humidity, wind speed, time of day, and day of the week. Using environmental data, a machine learning model is trained to find trends between ideal device selection and ambient variables. A web-based application created with Python and the Django framework incorporates the trained model, enabling users to enter environmental parameters and receive real-time device recommendations along with prediction confidence. The findings of the experiment show that the suggested approach can successfully help reduce wasteful energy use while preserving comfortable indoor conditions. This clever strategy promotes sustainable smart building management and increases energy efficiency.
Ekambaram et al. (Thu,) studied this question.