Enterprise electricity consumption analysis and prediction method based on data mining

Abstract

The invention relates to an enterprise electricity consumption analysis and prediction method based on data mining. Multiple influence factors, including temperature, moisture, holidays and the like,are combined to analyze and predict the enterprise electricity consumption. The method comprises the following steps that: firstly, utilizing a Newton interpolation method, a normalization method anda PAA (Piecewise Aggregate Approximation) algorithm are used for carrying out preprocessing; then, utilizing a spectral clustering algorithm to carry out clustering on a dataset, judging and correcting an abnormal data to obtain enterprise electricity consumption groups, including temperature, moisture, holidays and the like with high correlation; and finally, selecting the same class of enterprise electricity consumption data and the influence factors with high correlation as the prediction input of the model, and utilizing an RNN (Recurrent Neural Network) to obtain a prediction value. By use of the method, according to different enterprise electricity consumption types, an electricity consumption influence factor is combined to construct different prediction models, and therefore, the effects of high model prediction accuracy and data preprocessing ability can be achieved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a method for analyzing and predicting power consumption of enterprises based on data mining. Background technique [0002] In social and economic development, electric energy plays a vital role, and various studies and surveys directly link electric energy consumption with national economic, technological and social development. On the one hand, the demand for electric energy is increasing exponentially, and available resources are being consumed at an alarming rate; on the other hand, electric energy is still in short supply, and energy saving is a basic need. Therefore, power management should be strengthened and power usage should be optimized to reduce production costs and environmental hazards. Power consumption analysis and forecasting are important means to achieve this goal. [0003] At present, some research work on power forecasting has been carried out...

AI Technical Summary

Problems solved by technology

[0004] In the traditional power system prediction, most of them are only a single model, which cannot preprocess the data, let alone extract the most essential features of the data, resulting in low prediction accuracy

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