Research On Energy-saving Control System Of Wall-mounted Inverter Air Conditioner

Energy is an important material guarantee for human survival,and the world’s energy consumption is rising year by year.As an air conditioning appliance,air conditioners have a wide range of application areas and their energy consumption accounts for a high proportion of total social energy consumption.Therefore,the research of energy-saving control for air conditioners is valuable for energy saving and carbon emission reduction.In this thesis,we take wall-mounted inverter air conditioner control system as the research object,and design the monitoring system combined with deep learning algorithm to identify the control mode of air conditioner,and optimize the air conditioner temperature control by improved PID control to realize the intelligence and energy consumption reduction of air conditioner.The main research contents of this thesis are as follows:1.make an introduction to the system composition,working principle and energy saving principle of wall-mounted inverter air conditioner.Design the air conditioner indoor air quality monitoring system.The system architecture design includes hardware platform design,software layer design and cloud platform design.Sensors collect temperature,humidity and air quality parameters,the software layer performs data analysis and intelligent decision processing,and the cloud platform provides a visualization interface and control port,users can view indoor air quality status information and learn air conditioning commands to be able to achieve intelligent remote control of air conditioners to meet user comfort.2.A pattern recognition model based on genetic algorithm optimized back propagation neural network is constructed to learn the user’s habit of using air conditioner,which can automatically change the control strategy and update the threshold value in time to adapt to the user’s habit,to judge and adjust the air supply mode,dehumidification mode,cooling mode and heating mode of air conditioner.And combined with the target detection algorithm to identify the number of people in the room and the number of sleepers,to achieve the discrimination of air conditioning sleep mode,the number of people identification applied to the subsequent simulation design of energy-saving control system.3.The theoretical mathematical model of inverter air conditioner temperature control system is established,and the improved particle swarm optimized PID control algorithm is applied to the simplified air conditioner temperature control model,and Simulink simulation tests are conducted under heating and heating steady-state disturbances and cooling steady-state disturbances,and compared with fuzzy PID control and particle swarm optimized PID control,and the simulation results show that the improved particle swarm optimized PID control algorithm has better control effect and energy saving effect.Then the simulation is established to verify the energy saving control system jointly constructed by the number of people identification and the improved particle swarm optimized PID,and tested under three conditions of increasing the number of people in the room,decreasing the number of people in the room and frequent changes in the number of people in the room,and the simulation results show that the control effect can achieve effective suppression of temperature disturbances caused by changes in the number of people in the room.Through the model validation analysis,the feasibility of the air conditioning pattern recognition and the improved particle swarm optimized PID control strategy is illustrated,which has some reference significance for the energy saving of air conditioning.