Short-term load forecast method for electric power system based on deeply recursive neural network

A recurrent neural network, short-term load forecasting technology, applied in biological neural network models, forecasting, neural architecture, etc., can solve problems such as difficulty in accurate forecasting

Inactive Publication Date: 2018-03-06
ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER +1
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Problems solved by technology

Due to such characteristics of short-term load,

Method used

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  • Short-term load forecast method for electric power system based on deeply recursive neural network
  • Short-term load forecast method for electric power system based on deeply recursive neural network
  • Short-term load forecast method for electric power system based on deeply recursive neural network

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[0044] Attached Figure 1-3 , To further explain the technical solution.

[0045] Step 1: Collect and summarize data such as grid load data and meteorological data in historical areas, and import them into the Excel database.

[0046] Step 2: Data preprocessing. In order to avoid neuron saturation, the original load data needs to be preprocessed. This will help the convergence of the training process and improve the prediction accuracy. The main preprocessing method is to count the maximum and minimum values ​​of the historical load data in the training sample set, and normalize the load data to the [-1,1] interval, so that the data can be at the same level and speed up the neural network convergence .

[0047] Step 3: Determine the model structure.

[0048] DNN (Deep Neural Network) has a multi-hidden-layer structure, which repeatedly trains the input vector of the network to improve the accuracy of classification or prediction. The DNN prediction model is composed of an input lay...

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Abstract

The invention belongs to the technical field of short-term load forecast of an electric power system and discloses a short-term load forecast method for an electric power system based on a deeply recursive neural network. The short-term load forecast method includes (1), collecting historical power grid load and meteorological data and building a base for standby use; (2), getting rid of abnormaldata obtained in the step (1) and subjecting residual data to normalization processing; (3), determining a model structure with feedforward and feedback functions; (4), training a DRNN forecast modelbased on an IPSO algorithm through historical data; (5), using the DRNN forecast model based on the IPSO algorithm for forecast of actual load. The short-term load forecast method has the advantages that relevance layers are added on the basis of a multi-hidden-layer structure of a deep neural network, and an improved particle swarm algorithm is used as an optimized learning algorithm of the network to deeply optimize a model weight space; errors are decreased effectively, feedforward and feedback connection can be fused, the network generalization ability is improved and the load forecast precision is enhanced effectively.

Description

technical field [0001] The invention relates to the technical field of power system short-term load forecasting, in particular to a power system short-term load forecasting method based on a deep recursive neural network. Background technique [0002] As the scale and complexity of power systems continue to increase, the accuracy of short-term load forecasting in power systems plays a key role in effectively reducing power generation costs and implementing optimal control of power systems in various regions. Compared with long-term load forecasting, short-term load forecasting is mainly used to arrange power generation planning, and has the highest timeliness. Its load changes quickly and is greatly affected by abrupt factors such as temperature difference and humidity, and belongs to a dynamic nonlinear time series. Due to such characteristics of short-term load, it is difficult to achieve accurate forecasting. With the implementation of the new electricity reform, the co...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045
Inventor 林霞李可田凤字孔令元张智晟
Owner ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER
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