In this paper, two basic steps are taken to optimize and manage the energy of the smart home . In the first step, To provide a mathematical model and energy pattern to determine the temperature of the air conditioner thermostat with regard to climate change, so that ultimately the cost of consumed electricity is minimized and the welfare level of the smart home inhabitants will not fall below the definition. For this purpose, the neural network method has been estimated to have an instantaneous price for electricity in the coming days, with external temperature information outside the home and the current price of electricity in recent days. Then, using the PSO algorithm, the thermostat setting temperature is determined to optimize energy consumption and minimize the cost of consumable electricity. In the second step, while extracting the equivalent electric vehicle load and power consumption to charge it, the technical and economical analysis of providing smart home power supply through the storage battery of the electric vehicle is considered, so that according to the instantaneous electricity price calculated in the first step At a time when the cost of purchasing electric power from the electric network is high, the battery will provide electric power to the smart home power. Economic analysis results show savings on the cost of purchasing electrical energy with the proposed idea of this paper.