The invention relates to the technical field of
data processing, and particularly provides an intelligent prediction method,
system and device for industrial
power consumption and a storage medium, and the method comprises the steps: collecting
power consumption load, production operation, environment and time calendar multi-
source data, carrying out the fusion, cleaning and abnormal value
processing, constructing a preprocessing
time series data set, and generating state marking features in the abnormal
processing;
feature engineering is carried out on the
data set, and lagging, sliding statistics, trend, periodicity and external causal features are extracted; standardizing the numeric features, combining the standardized numeric features with the category features, and constructing a model input
feature set; and inputting the set into a pre-trained
hybrid prediction model, fusing output results of the
time sequence deep learning model and the integrated learning model therein, and finally outputting point prediction and
interval prediction of the industrial
electrical load. According to the method, through multi-source fusion and refined
feature engineering, the
cognition and prediction precision of an industrial
power consumption complex mode is remarkably improved.