The Future Energy Saving Through IoT Home Automation By Machine Learning

In residential settings, the increasing energy consumption necessitates the need for smarter approaches in automation systems rather than simple approaches automation. We introduces smart home energy management systems that pairs IoT (Internet of Things) sensing with machine learning for predictive control automation. The system uses multiple sensor such as temperature, humidity, and occupancy to acquire real time situational data. In the cloud, machine learning (ML) models control data combined with historical data to determine energy predictive control of high-saving appliances including HVAC and water heater to determine predictive control in the systems. The system deployed using ESP8266 microcontrollers and the Home Assistant platform hybrid edge-cloud architecture and has been functionality tested for one month. There are real world results which indicate improved performance in energy waste reduction of around 20%. The results are obtained while keeping the baseline scenario of user comfort and energy waste reduction constant. This highlights the multifunctional integrated and the flexibility of the combined IoT and ML systems. This innovation constitutes a decrease in the energy costs and the associated carbon emissions of a working household.