Enterprise electricity consumption prediction method

A forecasting method and electricity consumption technology, applied in forecasting, neural learning methods, data processing applications, etc., can solve the problems of difficulty in obtaining forecasting results, a large number of forecasting samples, and the impact of data sample size on modeling effects, and achieve the solution of electricity forecasting. Insufficient amount of data, saving training time, and improving the effect of prediction accuracy

Inactive Publication Date: 2022-03-11
CHONGQING TECH & BUSINESS UNIV
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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

Method used

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  • Enterprise electricity consumption prediction method
  • Enterprise electricity consumption prediction method
  • Enterprise electricity consumption prediction method

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Embodiment Construction

[0048] The present invention will be further described below with reference to the accompanying drawings and examples.

[0049] Such as figure 1 and figure 2 As shown, an enterprise power supply prediction method includes the following steps:

[0050] 1) Data collection, a company that determines a known power data, as a source domain of migration learning, is the source domain enterprise, collect source domain's historical power data, forming original sequence, as a source domain of migration learning, for historical power information Recorded power time sequence standardization process. In this embodiment, the amount of power data is a standardized processing of at least one year, and the standardization process is a standardized processing of Z-Score.

[0051] 2) Introducing the maximum mean difference, the maximum mean difference distance MMD is used as the similarity metric of the source domain enterprise and the target domain enterprise power data; the target domain enterpri...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/08G06N3/04
CPCG06Q10/04G06Q50/06G06N3/08G06N3/048G06N3/044
Inventor 白云肖威谢晶晶李川曾波
Owner CHONGQING TECH & BUSINESS UNIV
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