Electric quantity consumption predicting method based on deep learning

Abstract

The invention discloses an electric quantity consumption predicting method based on deep learning. According to the invention, a deep learning model is able to train a BP network according to the historical data as of now so as to achieve a better predicting effect. The reasons behind choosing the deep learning for electric quantity predicting are that it has a non-linear adaptive information processing ability unique to the neural network, that it has a strong error tolerance, and that it can be applied to the dynamic analysis for electric quantity consumption and meets a plurality of integrated factors such as time regularity and event suddenness. In the method, first, the intelligently sensed current and voltage data are calculated as electric quantity consumption amount for the training of a neural network so as to predict the electric quantity consumption at a next period. The predicted electric quantity consumption amount and the statistic power using duration are fed back to the user so as to guide him or her to conserve power. The method of the invention is simple and practical in use and can be applied to a smart home system connected via Wifi, and the method can also be used to predict the electric quantity consumption in regional and urban power grids.

Description

technical field [0001] From the perspective of neural network, the present invention comprehensively analyzes factors affecting power consumption, and establishes a power consumption prediction model based on deep learning, specifically relates to a power consumption prediction method based on deep learning, which belongs to the field of communication technology. Background technique [0002] With the development of various industries, the power consumption in urban areas is also increasing, and the prediction of power consumption plays an increasingly important role in power system planning and operation. Modern science and technology are developing rapidly, and the research on predicting power consumption is also deepening. Power consumption forecasting needs to be based on a large amount of detailed data obtained through investigation and research, through correct theoretical guidance, to consider various factors that affect power consumption, and to use reliable methods ...

AI Technical Summary

Problems solved by technology

However, the existing power consumption prediction methods more or less have problems such as the decline in long-term prediction accuracy, insufficient regularity processing, or narrow application range.

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