The study aims to effectively reduce building energy consumption, improve the
utilization efficiency of building resources, reduce the emission of
pollutants and greenhouse gases, and protect the ecological environment. A
prediction model of heating ventilation air conditioning (HVAC) energy
consumption is established by using back propagation neural network (BPNN)
and adapted boosting (Adaboost) algorithm. Then, the HVAC system is
optimized by building information modeling (BIM). Finally, the effectiveness
of the urban intelligent HVAC optimization prediction model based on BIM and
artificial intelligence (AI) is further verified by simulation experiments.
The research shows that the error of the prediction model is reduced, the
accuracy is higher after the Adaboost algorithm is added to BPNN, and the
average prediction accuracy is 86%. When the BIM is combined with the
prediction model, the HVAC programme of hybrid cooling beam + variable air
volume reheating is taken as the optimal programme of HVAC system. The power
consumption and gas consumption of the programme are the least, and the CO2
emission is also the lowest. Programme 1 is compared with programme 3, and
the cost is saved by 37% and 15%, respectively. Through the combination of
BIM technology and AI technology, the energy consumption of HVAC is
effectively reduced, and the resource utilization rate is significantly
improved, which can provide theoretical basis for the research of
energy-saving equipment.