METHOD FOR DETERMINING PRE-COOLING TIME OF BUILDING BY USING INTELLIGENT CONTROL ALGORITHM WITH NEURAL NETWORK MODEL

摘要

PURPOSE: A method for controlling the reserved cooling of a building using an intelligent control algorithm by a neural network model is provided to control air conditioning equipment by the intelligent control algorithm by the neural network model, thereby maintaining an indoor space of the building pleasant simultaneously with saving energy utilized for air conditioning. CONSTITUTION: A method for controlling a decrease of indoor temperature of a building using an intelligent control algorithm by a neural network model is as follows. The neural network model having input variables of indoor temperature, outdoor temperature, an inclination of the indoor and outdoor temperature and an output variable of pre-cooling time is fetched(S101). Measuring variables including the indoor temperature and outdoor temperature are measured at a predetermined time interval(S102). A circulation variable including the inclinations of the indoor temperature and outdoor temperature is calculated based on the measuring variables(S104). The pre-cooling time at measurement timing is calculated by inputting the input variables into the neural network model(S132). A difference between the pre-cooling time and target setting time is calculated and if the difference is within a set value, the operation is performed according to a preset operation process(S133,S134,S135). The inside of the building is night-purged by inducing the outdoor air before the pre-cooling using the neural network model(S121,S122,S123,S124). [Reference numerals] (AA) Start; (BB,DD,FF) No; (CC,EE,GG) Yes; (HH) End; (S101) Fetch an adapted neural network model; (S102) Measure the indoor temperature, the indoor humidity, the outdoor temperature, and the outdoor humidity every one minute; (S103) No noise included in the measured data?; (S104) Store the indoor temperature and humidity and the outdoor temperature and humidity, and calculate and store the inclinations of the indoor and outdoor temperatures; (S121) Calculate enthalpy of the indoor and outdoor air; (S122) Enthalpy of the indoor air is greater than that of the outdoor air?; (S123) Operate an intake fan and an exhaust fan; (S124) Stop the operations of the intake fan and the exhaust fan; (S131) Input the stored data to the adapted neural network model; (S132) Calculate a pre-cooling time at each measurement time through recollection in neural networks; (S133) Calculate the difference between the calculated pre-cooling time and a target time; (S134) Difference between the calculated pre-cooling time and a target time is within a preset value?; (S135) Activate a cooling facility; (S136) Start the full power operations of a freezer, an air conditioner, and an FCU; (S137) Store the time, the indoor temperature, the inclination of indoor temperature, the outdoor temperature, and the inclination of outdoor temperature when starting the operations; (S138) Store the reaching time when reaching a set temperature; (S139) Relearn an adapted backpropagation neural network model by inputting learning data collected through driving; (S140) Store learning information on the backpropagation neural network model