A smart and intelligent energy management system (SIEMS) can make the existing system more reliable, robust, and cost-effective to deal with the present power scenario. This also initiates the necessity to develop a new dynamic tariff system and integrate renewable energy sources (RES) along with the conventional grid which in turn reduces carbon emission. Many countries have adopted different strategies for energy conservation based on energy conservation code, the main aim of which is to enforce design and development of household appliances for efficient power consumption. From data provided by utility sectors, it has been found that even in few countries 25% to 28% of the total energy is consumed by domestic sector and 45% of other residential loads also contribute a major part of the total domestic load consumed in any country. As a whole, residences, which consist of light electrical loads and home appliances constitute prosumers. The total energy consumed by prosumers is also intended by Bureau of energy efficiency, to make them fall under these codes (Energy Conservation Building Code) so that they can be treated on a scale of 1 star to 5 stars. This in turn will help in improving the energy performance index (EPI). In this paper, the optimal scheduling model of household appliances and energy storage system (ESS) has been designed based on the availability of renewable energy sources and newly proposed dynamic tariff system. To validate the efficiency of the model, we have comparatively evaluated the performance of the model with different feature selection methods and optimization techniques. A case study has been done and it has been found that the weighted K nearest neighbor method for feature selection and hybrid greedy particle swarm optimization (HGPSO) technique for optimization are performing better than other existing methods. The proposed model reflects the cost-effectiveness and improvement in EPI for prosumers.