Energy consumption prediction method based on LSTM neural network

Abstract

The invention discloses an energy consumption prediction method based on an LSTM neural network. The energy consumption prediction method comprises the following steps of: converting total energy consumption data, extracting energy consumption data sets, processing the energy consumption data set by data; normalizing each piece of energy consumption data in the energy consumption data set, establishing and training a learning model; and outputting a prediction result according to the trained learning model; according to the method, various types of energy high-frequency energy data can be effectively stored and efficiently managed, massive historical energy consumption data and weather data can be quickly queried and processed, and the problems that the energy consumption data is difficultto store and the query speed is low are effectively solved.

Description

technical field [0001] The invention relates to the field of energy consumption prediction, in particular to a method for energy consumption prediction using an LSTM neural network in the automobile production industry. Background technique [0002] Energy consumption prediction has been widely applied to iron and steel enterprises, paper enterprises, power systems, etc. However, it is rarely used in the automobile production industry. However, the production stage of automobile raw materials is the link with the largest energy consumption, followed by the automobile assembly stage, and finally the automobile painting stage. Natural gas and electricity are the most important types of energy consumption for automobile manufacturers. In the process of automobile production, a large amount of tap water, natural gas, high-temperature hot water, steam, electricity, compressed air, etc. are consumed. Automobile manufacturers are high-energy-consuming enterprises, and the energy ...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is that the traditional energy consumption forecasting method is difficult to mine multi-dimensional influencing factors, cannot be predicted in time and the accuracy is not high, and the human-influenced factors are large, so a kind of energy consumption based on LSTM (Long Short Term Memory, Long Short Term Memory) neural network is provided. method of prediction

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