Enterprise electricity consumption prediction method

Abstract

The invention discloses an enterprise electricity consumption prediction method. The method comprises the following steps: 1) data acquisition; 2) introducing the maximum mean value difference, and carrying out similarity analysis; 3) modeling a long short-term memory network of transfer learning; and 4) electric quantity prediction: predicting the electric quantity of the target domain enterprise at the future moment by using the improved model in the step 3). The problem of insufficient data volume during electric quantity prediction is effectively solved, the model training time is saved, and the electric quantity prediction precision is improved.

Description

Technical field [0001] The present invention relates to the field of electricity prediction, and more particularly to a method of electricity consolidation. Background technique [0002] Electricity prediction is an important part of the power system power generation plan. It is the necessary premise of reasonable arrangements such as electricity, power generation, transmission and power distribution. It is the basis of the economic operation of power system, an important part of corporate production management, is an auxiliary And improving the beneficial tools for decision-making processes such as corporate economy, environment. Due to many factors such as historical reasons, technical levels, my country's existing power supervision information level is generally low, and smart management has still had a large gap. Therefore, the precision prediction of electricity consumption has always been one of the key issues facing the power management department. [0003] In response to ...

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Problems solved by technology

[0003] In response to this problem, the forecasting models in the prior art are all trying to accurately predict electricity consumption, and the methods include regression analysis, trend extrapolation, moving average, machine learning, artificial neural network, etc., but the aforementioned methods require a large amount of forecasting Sample, the size of the data sample size directly affects the modeling effect

It is difficult to obtain more accurate prediction results when the data sample size is small

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