Against the backdrop of the escalating global energy and environmental crisis

accurate prediction of air conditioning energy consumption is crucial for formulating effective energy-saving policies

optimizing energy utilization

reducing energy pressure

and reducing carbon emissions

as air conditioning energy consumption accounts for a large proportion of the entire building energy consumption. A data-driven air conditioning energy consumption prediction method is proposed based on a probability model of air-conditioning occupant behavior. Firstly

based on the analysis of the relationship between air conditioning energy consumption and factors such as air-conditioning occupant behavior

environmental parameters

time

and building

a feature label system for building air conditioning energy efficiency analysis is further constructed

covering multiple dimensions such as air conditioning occupant behavior

environment

time

and building characteristics. Secondly

the air-conditioning occupant behavior probability（AOBP）model is introduced as a factor to reflect the realtime interaction between the building environment

air conditioning occupants

and energy systems. This model considers the effects of strategy

time

events

and external stimuli

thus providing a more comprehensive estimation of air conditioning usage. Finally

particle swarm optimization algorithm is utilized to optimize the long short term memory network（LSTM）and to predict energy consumption across various building types and air conditioning systems. The simulation experiment results show that the proposed data-driven air conditioning energy consumption prediction method has made significant progress in improving prediction performance

but the calculation time has also correspondingly increased.