Patent KR-101261198-B1 (KR)
Title: Method for controlling internal temperature falling time of building by using intelligent control algorithm
Inventor: YANG IN HO
IPC: F24F11/76
Filed: 2013-01-10
Granted: 2013-05-10
Abstract: PURPOSE: A method for controlling a decrease of indoor temperature of a building using an intelligent control algorithm by a neural network model is provided to control air conditioning equipment with 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, inclinations of the indoor and outdoor temperature and an output variable of reaching time that the indoor temperature becomes the target temperature is fetched(S101). Measuring variables including the inclinations of 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 reaching time at measurement timing is calculated by inputting the input variables into the neural network model(S105,S106). A difference between the reaching 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(S107). The reaching time is the heating temperature decreasing time required for reaching to a set allowable temperature at each measurement timing, and the concentration of the indoor air is measured at a preset time interval(S121). If the concentration of the CO2 of the indoor air is higher than the set value, ventilation is performed by operating an air conditioner(S123). [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 and the outdoor temperature every one minute; (S103) No noise included in the measured data?; (S104) Store the indoor and outdoor temperatures, and calculate and store the inclinations of the indoor and outdoor temperatures; (S105) Input the stored data to the adapted neural network model; (S106) Calculate a decreasing time at each measurement timing through recollection in neural networks; (S107) Calculate the difference between the calculated decreasing time and the target setting time; (S108) Difference between the calculated decreasing time and the target setting time is within a set value?; (S109) Stop the operations of a boiler, an air conditioner, and an FCU; (S110) Store the time, the indoor temperature, the inclination of indoor temperature, the outdoor temperature, and the inclination of outdoor temperature when stopping the operations; (S121) Specify the concentration of the CO2 of the indoor air at a preset time interval; (S122) Concentration of the CO2 of the indoor air is higher than a set value?; (S123) Reoperate the air conditioner; (S124) Stop the air conditioner when reaching the target time; (S131) Store the decreasing time when reaching a set temperature; (S132) Relearn an adapted backpropagation neural network model by inputting learning data collected through driving; (S133) Store learning information on the backpropagation neural network model