Energy consumption prediction method

A forecasting method and energy technology, applied in forecasting, instruments, computing models, etc., can solve the problems that the sequence contains a lot of noise, and the energy consumption forecast is easily interfered by external factors, and achieves a good degree of fitting, excellent global search ability, high efficiency The effect of computing performance

Inactive Publication Date: 2018-12-25
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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AI Technical Summary

Problems solved by technology

[0005] However, since energy consumption prediction is easily disturbed by external factors, and the sequence contains a lot of noise, how to accurately predict energy consumption is still a technical problem that needs to be solved.

Method used

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Embodiment Construction

[0040] The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0041] 1. Empirical mode decomposition

[0042] (1)EMD

[0043] The EMD (Empirical Mode Decomposition) algorithm decomposes complex signals into several IMFs (Intrinsic Mode Functions) by screening methods, and IMFs represent the intrinsic characteristic vibration form of the signal. The IMF component must meet the following two conditions:

[0044] 1) The number of extreme points and the number of zero-crossing points are the same or differ by at most 1;

[0045] 2) The upper and lower envelopes should be partially symmetrical.

[0046] For a signal X(t), the flow of the EMD algorithm is as follows:

[0047] 1) Determine all maximum and minimum points of the signal X(t);

[0048] 2) According to the maximum and minimum points of the signal, use the cubic spline interpolation method to construct the f of the upper and lower envelopes of X(t) respectively a (t) and f ...

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Abstract

The invention belongs to the technical field of energy prediction, in particular to an energy consumption prediction method. The method comprises the following steps: collecting sample data includinghistorical energy consumption, population, GDP, industrial structure, energy consumption structure, energy intensity, carbon emission intensity and total import and export amount; the sample data being subjected to dimensionless processing, and the grey relational degree of each sample data and energy consumption structure being calculated, and the input factors of the model being selected according to the order of grey relational degree; multiple IMF components being obtained by integrated empirical mode decomposition (EMD) based sequence denoising; the parameters of LS-SVM being optimized byusing the improved hybrid frog leapfrog algorithm, and the forecasting model being established to reconstruct the forecasting results, and the final energy consumption forecasting results being obtained. Experiments proved the EMD-ISFLA-LSSVM model predicts the energy consumption, and the predicting effect is remarkable.

Description

technical field [0001] The invention belongs to the technical field of energy forecasting, and in particular relates to an energy consumption forecasting method. Background technique [0002] Energy is an important material basis for social development, and its consumption affects the stable and sustainable development of the natural environment and economy. With the rapid development of China's economy, its energy consumption continues to rise, and it has become the world's largest energy consumer. Therefore, by establishing a suitable energy consumption prediction model and accurately predicting the total energy consumption, it can provide a scientific basis for my country to formulate a reasonable energy production plan and policies related to energy conservation and emission reduction, which is of great importance to promote the sustainable development of the world economy and environment. practical significance. [0003] The energy system is a complex nonlinear system,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/00
CPCG06N3/006G06Q10/04
Inventor 牛东晓戴舒羽厉艳李偲
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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