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Power load short-term prediction method based on EWT neural network

A technology of power load and forecasting method, which is applied in the field of short-term power load forecasting based on EWT neural network, can solve the problem that the accuracy of power load forecasting needs to be discussed, and achieve the effect of improving the accuracy

Pending Publication Date: 2021-01-08
NINGBO LIXIN DISTRIBUTING CABINET WORKS
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Problems solved by technology

However, the traditional Elman neural network cannot realize the frequency characteristic decomposition of power load data, and the accuracy of its power load forecasting is still open to question

Method used

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  • Power load short-term prediction method based on EWT neural network
  • Power load short-term prediction method based on EWT neural network
  • Power load short-term prediction method based on EWT neural network

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Embodiment Construction

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0032] A short-term electric load forecasting method based on EWT neural network, comprising the following steps:

[0033] Step 1. Combine the hourly power load data in the power supply area into a column vector x∈R N×1 , and standardize it according to the formula shown below to obtain a column vector

[0034]

[0035] In the above formula, μ and δ respectively represent the mean and standard deviation of all elements in the column vector x, R N×1 Represents an N×1-dimensional real vector;

[0036] Step 2. Perform data processing through MATLAB tools, and use the EWT tool among them to convert the column vector Convert to D sub-signal vectors z 1 ,z 2 ,…,z D , and construct the input matrix X according to the formula shown below 1 ,X 2 ,...,X D with the output matrix Y 1 ,Y 2 ,...,Y D :

[0037]

[0038]

[0039] In the...

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Abstract

The invention discloses a power load short-time prediction method based on an EWT neural network, and the method is characterized in that the power load data measured in a power supply region per hourforms a column vector, the column vector is acquired by standardization according to a formula shown in the specification, and the column vector is converted into D sub-signal vectors by EWT, normalization is performed, each row vector in a new input matrix is inputted; meanwhile, each row vector in the new input matrix is outputted; an Elman neural network model is created; a transfer function of middle layer neurons is determined; a BP algorithm is used to train the Elman neural network model and establish a short-term power load forecasting model, 168 pieces of power load data in the latest seven days is used to predict the power load data in the next day; the invention has the advantages that the prediction of the power load data of 24 hours of the next day is realized by power load data of the previous 7 days, and prediction accuracy is improved.

Description

technical field [0001] The invention relates to a power load forecasting method, in particular to a short-term power load forecasting method based on an EWT neural network. Background technique [0002] The role of short-term power load forecasting is mainly to predict the power load situation in the next day through the power load data of a certain area or region. The accuracy of short-term power load forecasting has important research significance for the safety and economic benefits of power systems. Especially in the capital free market with fierce competition in electricity, the accuracy of short-term power load forecasting is the key to optimize the management ability of power system. However, the dynamic change of power load is very complex, and the power load will be affected by many factors, such as weather data. Therefore, to establish an appropriate short-term forecasting model, it is necessary to take into account the complex characteristics of power load change...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06F17/16G06F17/18G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084G06F17/16G06F17/18G06N3/045Y04S10/50
Inventor 章伟斌史旭华蓝艇
Owner NINGBO LIXIN DISTRIBUTING CABINET WORKS
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