The development of ‘demand-side load management’ is the outcome of the smart grid initiative. Due to the significant amount of loads in the residential sector, home energy management has received increasing interest. In the country like India, we are lagging behind, in the power sector as the demand is much more than the supply. Moreover, there is not a single initiative, which has been taken for the deployment of smart suppliers and smart users. Here, I propose a hardware design of smart home energy management system (SHEMS). With the help of this proposed design, it is possible to have a real-time, price-responsive control strategy for domestic loads such as electrical water heater (EWH), illumination (Lights), air conditioning (Fan), dryer etc. Consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. This system is designed with sensors to detect human activities and the behavior is predicted by applying a machine learning algorithm in order to help consumers reduce total payment on electricity. Finally, for the verification of the hardware system, simulation and experiment results will be checked based on an actual SHEMS prototype.