Predicting method for short-term electric load of Seq2seq network based on multi-layer Bi-GRU

A technology of short-term power load and forecasting methods, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as complex process and low forecasting accuracy, achieve good robustness and forecasting accuracy, improve learning efficiency and forecasting level Effect

Inactive Publication Date: 2018-06-22
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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However, the above algorithms all have the disadvantages of low prediction accuracy and complicated process.

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  • Predicting method for short-term electric load of Seq2seq network based on multi-layer Bi-GRU
  • Predicting method for short-term electric load of Seq2seq network based on multi-layer Bi-GRU
  • Predicting method for short-term electric load of Seq2seq network based on multi-layer Bi-GRU

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[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0025] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention discloses a predicting method for short-term electric load of a Seq2seq network based on a multi-layer Bi-GRU, similar daily samples are extracted by using FCM method and the input variables are standardized by the Min max standardization method. The multi-level Seq2seq neural network structure is constructed with Bi GRU neurons as the basic unit. At the same time, the SELU activation function is selected as the output layer activation function of the whole neural network to reduce gradient vanishing and gradient explosion, to realize smooth operation of the whole model in the training process.

Description

technical field [0001] The invention relates to a short-term power load forecasting method of a Seq2seq network based on multi-layer Bi-GRU. Background technique [0002] In the power industry, short-term power load forecasting is mainly used to arrange power system generation mix and optimize load scheduling. Accurate load forecasting is the key to ensuring the balance between supply and demand of the power grid, and it is also an important prerequisite for ensuring the safe, stable and economical operation of the power grid. According to the prediction results, the power generation plan can be reasonably arranged, so that the system can maintain the minimum operating cost within the safe range. [0003] The traditional short-term power load forecasting methods mainly include regression analysis method, trend extrapolation method, expert system method and time series method. Among them: the trend extrapolation method and the regression analysis method are mainly applicabl...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 路宽苏建军赵岩毕贞福王昕孟祥荣高嵩孙雯雪庞向坤赵阳王文宽李军韩英昆于庆彬姚长青李克雷颜庆
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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