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Short-term power load prediction model establishment method based on EMD-VMD-PSO-BPNN

A technology of EMD-VMD-PSO-BPNN, short-term power load, applied in the direction of forecasting, neural learning method, biological neural network model, etc., can solve the problems that the power load is not periodic, and the method of forecasting model is not suitable for paper-making enterprises, etc.

Pending Publication Date: 2019-07-12
广州博依特智能信息科技有限公司
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Problems solved by technology

However, environmental factors such as temperature have little impact on the power load of paper-making enterprises, and the power load is not cyclical, and no research has yet shown what the key factors affecting paper-making enterprises are, so a prediction model is established based on periodicity and key influencing factors The method is not suitable for paper companies

Method used

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  • Short-term power load prediction model establishment method based on EMD-VMD-PSO-BPNN
  • Short-term power load prediction model establishment method based on EMD-VMD-PSO-BPNN
  • Short-term power load prediction model establishment method based on EMD-VMD-PSO-BPNN

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Embodiment 1

[0081] This embodiment discloses a method for establishing a short-term power load forecasting model based on EMD-VMD-PSO-BPNN, which uses splitting and reconstruction to predict the power load trend in the next hour, including the following steps:

[0082] Such as figure 1 As shown, a short-term power load forecasting method based on EMD-VMD-PSO-BPNN includes the following steps:

[0083] S1. Obtain electricity consumption data with qualified data quality of papermaking enterprises.

[0084] Using the historical electricity consumption data saved in the historical database of the energy management system of the papermaking enterprise, obtain the electricity load data of two months.

[0085] S2. Using the EMD-VMD decomposition algorithm, perform sequence decomposition on the preprocessed load sequence.

[0086] The power consumption of papermaking enterprises often fluctuates greatly, and the general prediction model is not good for predicting large fluctuations in data fluctu...

Embodiment 2

[0143] A method for establishing a short-term power load forecasting model based on EMD-VMD-PSO-BPNN, including the following modeling and model evaluation steps:

[0144] 1. Obtain two-month total electricity load data from the historical database of a papermaking enterprise, such as image 3 shown. The first 75% of the sequence is used as the training set, and the last 25% is used as the test set.

[0145] 2. Split the data of the training set through the EMD-VMD decomposition model. After splitting, there are 14 sequences in total, such as Figure 4 middle Figure 4 (a)~ Figure 4 (n) shown.

[0146] 3. The split sequence is similarly reconstructed by the approximate entropy algorithm, and the approximate entropy values ​​of different sequences are shown in Table 1.

[0147] Table 1. Decomposition sequence approximate entropy values

[0148] entropy value serial number 2.49E-05 4 6.97E-04 1 8.60E-04 7 3.97E-03 9 4.33E-03 12 1.6...

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Abstract

The invention discloses a short-term power load prediction model establishment method based on EMD-VMD-PSO-BPNN. The short-term power load prediction model is applied to power load prediction of a papermaking enterprise, and comprises the following steps: firstly, obtaining data of a total effective load with qualified data quality of the papermaking enterprise; performing sequence decomposition on the total effective load by adopting an EMD-VMD combination algorithm; reconstructing the decomposed sequence by adopting approximate entropy; selecting a model to input by using a lagging autocorrelation method; adopting PSO-BPNN to model the reconstructed sequence; and training the PSO-BPNN model by adopting the training sample, establishing a prediction model, predicting the power consumptionload of the papermaking enterprise, and finally analyzing the prediction effect. The short-term power load prediction model is established based on the EMD-VMD-PSO-BPNN algorithm, and the method hasthe advantages of being fast in model convergence, high in prediction result precision, free of lag and the like.

Description

technical field [0001] The invention relates to the technical field of intelligent power consumption in papermaking enterprises, in particular to a method for establishing a short-term power load forecasting model for papermaking enterprises based on EMD-VMD-PSO-BPNN. Background technique [0002] A large amount of power equipment is required in the papermaking process, which is why electric energy has become the most important energy source in the papermaking process. Since there are a large number of intermittent equipment in these energy equipment, a reasonable production scheduling plan can not only effectively improve equipment utilization efficiency and reduce energy consumption, but also realize intelligent and power purchase through peak-shifting power consumption, reduce power purchase, and reduce energy consumption. Cost of production. By predicting the electricity load in the production process, at the same time, establishing a high-accuracy short-term electricit...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/08
CPCG06Q10/04G06Q10/067G06Q50/06G06N3/084
Inventor 李继庚洪蒙纳满奕胡雨沙
Owner 广州博依特智能信息科技有限公司
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