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A short-term power load forecasting method based on twin support vector machines

A short-term power load, support vector machine technology, applied in forecasting, computer components, data processing applications, etc., to achieve the effect of improving convergence speed, improving performance of optimization, and fast learning efficiency

Active Publication Date: 2021-09-24
NORTHEASTERN UNIV LIAONING
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Problems solved by technology

Each method has its own limitations, and currently no method is suitable for load forecasting under the conditions used

Method used

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  • A short-term power load forecasting method based on twin support vector machines
  • A short-term power load forecasting method based on twin support vector machines
  • A short-term power load forecasting method based on twin support vector machines

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Embodiment Construction

[0098] The present invention will be further elaborated below in conjunction with the accompanying drawings of the description.

[0099] The invention provides a short-term power load forecasting method based on twin support vector machines. TWSVM is developed on the basis of SVM. It is a new type of machine learning algorithm, which has faster learning efficiency and stronger promotion ability than SVM. The invention uses TSVR to establish a short-term power load forecasting model, and proposes a DW-TSVR algorithm based on LBSA optimization for the problems of forecasting accuracy and forecasting efficiency of load forecasting.

[0100] Such as figure 1 Shown, a kind of short-term power load forecasting method based on twin support vector machine of the present invention comprises the following steps:

[0101] 1) Make a sample data set according to the format of the model input vector;

[0102] 2) Preprocess the data of the sample data set, which is divided into two catego...

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Abstract

The invention relates to a short-term power load forecasting method based on a twin support vector machine, comprising the following steps: 1) making a sample data set according to the format of the model input vector; 2) classifying the data of the sample data set, one class is used for parameter optimization, and another type is used for testing; normalized preprocessing is performed on the parameter optimized type; 3) LBSA is used to optimize the DW-TSVR parameters for the sample data after normalized preprocessing; 4) the parameter optimization based on LBSA is used The DW‑TSVR algorithm is combined with another type of sample data used for testing to verify the DW‑TSVR model and calculate the forecast results of short-term power loads. The present invention introduces Levi's flight into the flight behavior of the BSA algorithm, and proposes the LBSA algorithm. The bird swarm algorithm has better performance.

Description

technical field [0001] The invention relates to a grid load forecasting technology, in particular to a short-term power load forecasting method based on a twin support vector machine. Background technique [0002] Load forecasting plays an important role in the daily work of power production department and management department. Accurate load forecasting is an important basis for ensuring the smooth operation of the power system. Power systems bring together power plants, transmission and distribution grids, and end users into a complex network. Electricity is a special commodity that is invisible and colorless and cannot be seen with the naked eye. After electricity is produced, it will be transmitted along the transmission and distribution grid to the terminal, and then consumed. This process takes an extremely short amount of time, almost simultaneously, to complete. Due to the particularity of electric energy, large-scale storage of electric energy will outweigh the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/2411G06F18/214
Inventor 翟莹莹高若涵吕振辽敖志广薛丽芳
Owner NORTHEASTERN UNIV LIAONING
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