METHOD AND DEVICE FOR PREDICTING THERMAL LOAD OF ELECTRICAL SYSTEM

A method and device for predicting the thermal load of an electrical system. 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; S3: dividing the acquired data daily reference line into multiple time sections; 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; S6: inputting the similarity sequence matrix into an extreme learning machine (ELM) for training, acquiring a prediction model, and predicting the thermal load of the electrical system. The method effectively retains important change trend information in a time sequence of a thermal load by using a time sequence representation method based on trend segmentation, thereby being capable of more accurately predicting the change trend of the thermal load.