Simulation of low energy building thermal energy cycle in IoT smart city planning based on environmental sensors and deep learning

Abstract

With the intensification of global climate change, the reduction of building energy consumption has become an important goal of smart city development. By optimizing the thermal energy circulation system, low energy buildings can not only reduce energy consumption, but also improve living comfort. In recent years, with the help of environmental sensors and deep learning technology, the intelligent management of building heat energy cycle has become a research hotspot. The research constructs an intelligent thermal energy circulation system that integrates multiple environmental sensors for real-time monitoring of indoor and outdoor temperature, humidity and other key environmental parameters. A deep learning algorithm is used to analyze the collected data to optimize the control strategy of the thermal energy cycle. Through simulation, the energy efficiency performance of the scheme under different climatic conditions and building types was evaluated. The experimental results show that the control strategy based on environmental sensors and deep learning can significantly improve the thermal energy utilization efficiency of low-energy buildings, and the average energy consumption is greatly reduced compared with the traditional management mode. The system shows good adaptability and stability under different climate conditions. Therefore, the application of environmental sensors and deep learning technology in the thermal energy cycle of low-energy buildings can effectively promote the energy efficiency management of smart cities.