Electric quantity sale prediction method based on long and short term memory network

A long-short-term memory and prediction method technology, which is applied in the direction of prediction, instrumentation, data processing applications, etc., can solve the problems of RNN gradient disappearance and the inability to make good use of long-term historical information, etc., to achieve accurate prediction and improve accuracy.

Active Publication Date: 2018-04-27
LISHUI POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER +1
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Problems solved by technology

[0007] However, RNN has the problem of gradient disappearance, which ma

Method used

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  • Electric quantity sale prediction method based on long and short term memory network
  • Electric quantity sale prediction method based on long and short term memory network
  • Electric quantity sale prediction method based on long and short term memory network

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Embodiment

[0045] Embodiment: In this embodiment, a method for forecasting electricity sales based on a long-short-term memory network, such as figure 1 shown, including the following steps:

[0046] S1: Determine the influencing factors affecting electricity sales data;

[0047] S2: Calculate the Pearson correlation coefficient r between the electricity sales data of each industry to be analyzed and the data of each influencing factor, and construct a correlation coefficient matrix;

[0048] The calculation formula of Pearson correlation coefficient r is as follows:

[0049]

[0050] Among them, r represents the Pearson correlation coefficient, the value range is [-1, 1], r=0 means no correlation, the closer the r value is to 1, the greater the positive correlation, and the closer the r value is to -1, the greater the negative correlation. Large; x and y are two data characteristic variables; Cov(x, y) means covariance, σx, σy means standard deviation;

[0051] Through the above c...

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Abstract

The invention discloses an electric quantity sale prediction method based on a long and short term memory network. The method comprises the following steps of S1, determining an influence factor influencing electric quantity sale data; S2, calculating a Pearson correlation coefficient r of electric quantity sale data of an industry to be analyzed and data of each influence factor; S3, using a k-means cluster algorithm, clustering the Pearson correlation coefficient r of each industry and acquiring several clusters after clustering; S4, carrying out normalization processing on daily total electricity consumption data of each cluster and carrying out normalization processing on the data of each influence factor; and S5, based on an electric quantity sale prediction model of a long and shortterm memory network LSTM, acquiring a total electric quantity sale prediction result. In the invention, electric quantity sale data and data characteristics of correlation influence factors can be automatically learned, and based on the long and short term memory network, multi-condition electric quantity sale data is modeled so as to realize accurate prediction of electric quantity sale.

Description

technical field [0001] The invention relates to the technical field of electricity sales forecasting, in particular to a method for forecasting electricity sales based on a long-short-term memory network. Background technique [0002] With the rapid development of the national economy, the electric power industry is also in the stage of vigorous development. Accurate electricity sales forecast is of great significance for power supply companies to adjust future power supply, optimize power supply structure, and improve the safety of power system operation. [0003] The essence of electricity sales forecasting problem can be attributed to the time series forecasting problem. Time series refers to the sequence in which the values ​​represented by a certain statistical indicator at different times are arranged in the order of their occurrence, and time series data is the actual data reflected by the time series. Since the change of electricity sales is not only related to the ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 黎自若方志强王晓辉夏通周艳梅付健艺施进平周晨吴志华吴中旻朱好吴昊铮严辉敏
Owner LISHUI POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER
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