Simulation and Application of Low Carbon Heating Buildings Based on Genetic Algorithm Optimization PID

As technologies related to building heat production and low carbon develop, the target requirements for heat production and low carbon building models are being optimized. The design and research of low-carbon(LC) building models for heating based on genetic algorithm optimization PID (Proposal Integral Differential) is becoming increasingly important. How to reduce the carbon emissions of the building optimization model and improve the heat transfer performance of the building are currently key issues that need to be urgently addressed in the construction of the entire LC building model for heating. This article focuses on the prediction of building carbon emissions and related research on LC emission systems for building heating. By utilizing genetic algorithms to optimize the application of PID in buildings and analyzing the algorithmic process steps, combined with the weight vectors and normalization calculations of indicators related to LC emission systems for building heating, the following conclusions are drawn based on the data results: Five types of building samples are selected through simulation experiments. The application of a LC building model for heating based on genetic algorithm optimization PID has improved carbon emissions compared to traditional schemes, with a comprehensive average reduction of about 13.5%. At the same time, it has also improved building heat transfer performance, with a comprehensive average improvement of about 7.7%. This indicates that the heating low-carbon building model based on genetic algorithm optimized PID has a good practical application effect in low-carbon heating of buildings.