Analyse The Energy Consumption by Integrating the IOT and Pattern Recognition Technique

High power costs and end-use electricity usage are driving energy consumption optimization and data-driven demand-side management. The consumer action and the approach used to energy management have a significant impact on how well these systems work. It is crucial to provide comprehensive energy-consumption information models down to the appliance level to educate the public and encourage them to adopt more efficient energy habits. The Home Energy Management System's (HEMS) primary objective is to cultivate an energy-efficient environment that controls IoT devices via its network. With HEMS, customers may adjust their use to day-to-day price fluctuations, thus reducing energy expenses. expressive data-mining methods to create a numerical model that provides users with data about their home appliances. This data includes the number and length of operations, energy consumption over different periods based on 15-minute monitoring data, and the ability to disaggregate cycles for appliances with cyclic operation. Calibration and validation of the model were performed on two datasets acquired by ENEA via actual monitoring of the Italian home. Subsequent testing across many appliances demonstrated successful analysis of energyingesting decorations. Consequently, it has been included in the ENEA-developed DHOMUS IoT stage, which tracks and analyses residential energy use to raise public participation and knowledge about this issue. Encourage end users to make more ethical and sustainable energy use decisions and meet the current European Council Regulation (EU) 2021/1853 requirements by reducing energy consumption, according to the findings it shows that the model built is sufficiently accurate.