With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios by consumer behavior. In this study, an intelligent residential energy management architecture and algorithm to manage residential energy production/consumption are proposed. The algorithm controls the energy flow in the home according to real-time potential solar power estimation, demanded energy estimation, electricity consumption price, and battery state-of-charge (SoC). The fuzzy logic algorithm has been developed to determine the estimated comfort and cost-effectiveness ratios in the near future. The simulated annealing algorithm, a meta-heuristic algorithm, is performed to obtain the best operating point decision of the battery using the comfort and cost-effectiveness ratios. Energy flow direction and battery SoC are optimized using simulated annealing based on the comfort and cost-effectiveness ratio (comparison of alternatives with respect to multiple criteria of different levels of importance for energy usage). The focus is to generate maximum profit from energy sales for monthly profit to be achieved. Prototyped hardware and software are implemented and tested in real-time. The test results show that the 20% reduces energy consumption, and a monthly gain of $89.2 is obtained from energy sales using the proposed method. Therefore, the test results reveal the effectiveness of the proposed architecture and algorithm.