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Cyclic neural network short-period load predication method based on information entropy clustering and attenuation mechanism

A technology of cyclic neural network and short-term load forecasting, which is applied in forecasting, AC network circuits, computer components, etc., to achieve the effect of improving forecasting accuracy and accuracy

Inactive Publication Date: 2018-01-16
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

Since the load is affected by many characteristic attributes and unknown factors, no method can guarantee high-precision prediction results in all cases

Method used

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  • Cyclic neural network short-period load predication method based on information entropy clustering and attenuation mechanism
  • Cyclic neural network short-period load predication method based on information entropy clustering and attenuation mechanism
  • Cyclic neural network short-period load predication method based on information entropy clustering and attenuation mechanism

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Embodiment Construction

[0018] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0019] Step 1: Data preprocessing, determine the input feature variables.

[0020] Since the size of the power load is affected by many factors, in addition to the need for load data, characteristics such as season, temperature (°C), humidity (%), wind speed (m / s), rainfall, week type, and legal holidays all play a role effect. In the process of data analysis, the relationship coefficients of the peak load in the day before, the average load in the previous seven days, the load value in the seven days before, the load value in the same period of last year and the forecast date all show a certain correlation. At the same time, in order to ensure that the training samples are large enough, this ...

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Abstract

The invention provides a cyclic neural network short-period load predication method based on information entropy clustering and an attenuation mechanism. The method comprises the following steps of analyzing characteristics which affact the power load; calculating the information entropies of all characteristics to the load by means of an xgboost algorithm; performing cluster analysis based on theinformation entropy of each characteristic as the weight on the historical data of a predicated area by means of a clustering algorithm; selecting a cluster which is nearest to a predicating day weight from the clustering results, and forming a time sequence T according to a sequence that the time to the predicating time reduces from longest to shortest; using the time sequence T as an encoder ofthe attention cyclic neural network, and obtaining a predication result by a decoder. Compared with the prior art, the cyclic neural network short-period load predication method has advantages of high predication precision and high self-adaptability.

Description

technical field [0001] The invention relates to the technical field of electric power forecasting of a power grid, in particular to a short-term load forecasting method of a cyclic neural network based on feature information entropy clustering and an attention mechanism. Background technique [0002] Short-term load forecasting plays an important role in power control, security, market operation and rational dispatching plan of power grid. Short-term power load forecasting is mainly used to predict the power load usage in the next few hours, a day or a week. High-precision short-term load forecasting is conducive to reducing the economic cost of power grid operation, power system equipment scheduling and safety. Since the power load is affected by various factors, it is difficult to achieve high-precision load forecasting in the actual production process. [0003] In the short-term load forecasting model that has been mainly applied, it is mainly divided into traditional m...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62H02J3/00
Inventor 袁家斌郑慧婷
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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