A method for generating schedule data for a refrigerator, comprising: by a communication unit of a server, receiving pattern base data from two or more refrigerators during a learning unit period, wherein the pattern base data includes action data and time data, the action data of the pattern base data are data on actions performed by the refrigerator or data on whether or not the refrigerator has performed a load response, and the time data of the pattern base data are data on the number of times in which the refrigerator has been opened or closed within a unit time period or data on the number of times in which the refrigerator has operated a particular operation mode within a unit time period; the received pattern base data being inputted to a data set generation unit of the server; by the data set generation unit, generating a temporary learning data set optimized for the inputted pattern base data by changing all or a part of an existing first learning data set; by the communication unit and the data set generation unit, generating a second learning data set by repeating the step of receiving the pattern base data, the step of the pattern base data being inputted to the data set generation unit, and the step of generating the temporary learning data set; by the communication unit, transmitting the second learning data set to the refrigerators; and by one of the refrigerators, generating the schedule data including predicted action data and time data of the predicted action data for a prediction unit period by applying the second learning data set to the pattern base data during the learning unit period. by a communication unit of a server, receiving pattern base data from two or more refrigerators during a learning unit period, wherein the pattern base data includes action data and time data, the action data of the pattern base data are data on actions performed by the refrigerator or data on whether or not the refrigerator has performed a load response, and the time data of the pattern base data are data on the number of times in which the refrigerator has been opened or closed within a unit time period or data on the number of times in which the refrigerator has operated a particular operation mode within a unit time period; the received pattern base data being inputted to a data set generation unit of the server; by the data set generation unit, generating a temporary learning data set optimized for the inputted pattern base data by changing all or a part of an existing first learning data set; by the communication unit and the data set generation unit, generating a second learning data set by repeating the step of receiving the pattern base data, the step of the pattern base data being inputted to the data set generation unit, and the step of generating the temporary learning data set; by the communication unit, transmitting the second learning data set to the refrigerators; and by one of the refrigerators, generating the schedule data including predicted action data and time data of the predicted action data for a prediction unit period by applying the second learning data set to the pattern base data during the learning unit period.