A method for predicting a change trend of a thermal load of an electrical system by predicting the thermal load of the electrical system in next 24 hours, wherein the method comprises: S1: pre-processing historical daily data of the thermal load of an electrical system; S2: acquiring a data daily reference line according to the pre-processed historical daily data, taking a data mean of a preset number of days closest to a to-be-predicted day as the data daily reference line, wherein the preset number of days is N days with meteorological trend similarities between the to-be-predicted day and historical days ranking ahead, there are N pieces of historical data at a same moment, and the data mean is calculated for the same moment, and wherein N is greater than 3; S3: dividing the acquired data daily reference line into multiple time sections, wherein the data daily reference line is divided into multiple time sections according to extreme points in the data daily reference line, wherein in a day, there are two maximum values and two minimum values; or according to points with a difference between slopes of two adjacent points greater than a preset threshold and extreme points in the data daily reference line; S4: screening the historical daily data, and calculating a trend similarity value of the screened historical daily data and the data daily reference line within each divided time section respectively; S5: choosing historical daily data corresponding to a trend similarity value greater than a preset reference value to form a similarity sequence matrix; and S6: inputting the similarity sequence matrix into a constructed extreme learning machine (ELM) for training, acquiring a prediction model, and using the prediction model to predict the change trend of the thermal load of the electrical system. S1: pre-processing historical daily data of the thermal load of an electrical system; S2: acquiring a data daily reference line according to the pre-processed historical daily data, taking a data mean of a preset number of days closest to a to-be-predicted day as the data daily reference line, wherein the preset number of days is N days with meteorological trend similarities between the to-be-predicted day and historical days ranking ahead, there are N pieces of historical data at a same moment, and the data mean is calculated for the same moment, and wherein N is greater than 3; S3: dividing the acquired data daily reference line into multiple time sections, wherein the data daily reference line is divided into multiple time sections according to extreme points in the data daily reference line, wherein in a day, there are two maximum values and two minimum values; or according to points with a difference between slopes of two adjacent points greater than a preset threshold and extreme points in the data daily reference line; S4: screening the historical daily data, and calculating a trend similarity value of the screened historical daily data and the data daily reference line within each divided time section respectively; S5: choosing historical daily data corresponding to a trend similarity value greater than a preset reference value to form a similarity sequence matrix; and S6: inputting the similarity sequence matrix into a constructed extreme learning machine (ELM) for training, acquiring a prediction model, and using the prediction model to predict the change trend of the thermal load of the electrical system. <EOS>