This paper proposes a network architecture design for an intelligent residential energy-saving system based on IoT technology. The perception layer is constructed using ZigBee technology, while remote monitoring is achieved through Internet and GPRS technologies. The system encompasses functions such as residential security, intelligent control of lighting and temperature, individual household heat metering, and electricity consumption monitoring. An electricity consumption optimization model based on an improved genetic algorithm is established. Through field testing and data analysis, the effectiveness of the system in thermal environment regulation and energy consumpti on optimization is validated. During the cooling season, the largest proportion of indoor temperatures in smart housing is 26°C, accounting for 26.5%. In conventional housing, the largest proportion of indoor temperatures during the cooling season is 28°C, accounting for 17.5%. The relative humidity range in smart housing is slightly narrower than in conventional housing. During the heating season, the indoor thermal environment in smart housing remains significantly superior to that in conventional housing, and the IoT system significantly reduces the duration of humidity exceeding standards. Energy-saving benefits decrease as the proportion of factors increases, from 3% at 4,629.52 yuan/m² to 13% at 2,023.47 yuan/m², indicating that the economic benefits of reducing energy consumption decrease as the energy-saving proportion increases.
