Monthly power demand prediction method based on VMD-ANFIS-ARIMA

A technology for power demand and forecasting methods, applied in forecasting, neural learning methods, data processing applications, etc., can solve problems such as destroying the integrity of power demand data time series, to overcome uncertainty and volatility, reduce noise, improve The effect of prediction accuracy

Pending Publication Date: 2021-06-18
STATE GRID ZHEJIANG ELECTRIC POWER +1
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

These methods improve the prediction accuracy to a certain extent, but although these methods can handle nonlinear problems better, they will destroy the time series integrity of power demand data

Method used

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  • Monthly power demand prediction method based on VMD-ANFIS-ARIMA
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  • Monthly power demand prediction method based on VMD-ANFIS-ARIMA

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Experimental program
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Embodiment

[0058] A monthly electricity demand forecasting method based on VMD-ANFIS-ARIMA, such as figure 1 shown, including the following steps:

[0059] Step 1. Obtain monthly electricity consumption sequence data and determine the number of VMF components of VMD;

[0060] Step 2, using the screened influencing factors as independent variables and the trend items in VMF as dependent variables, use the ANFIS model to predict;

[0061] Step 3: Carry out sequence stationarity test on VMF other than the trend item, and determine the order of AR and MA according to its correlation coefficient and its partial autocorrelation coefficient;

[0062] Step 4, use the ARIMA model to perform time series forecasting of VMFs other than trend items;

[0063] Step 5, performing linear reconstruction on each VMD component prediction result to obtain the final power consumption demand prediction result.

[0064] Variational Mode Decomposition (Variational Mode Decomposition, VMD) is an adaptive, comp...

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Abstract

The invention discloses a monthly power demand prediction method based on VMD-ANFIS-ARIMA, which solves the defects in the prior art, and comprises the following steps: 1, obtaining monthly power consumption sequence data, and determining the number of VMFs of VMD components; 2, taking the screened influence factors as independent variables, taking trend terms in the VMF as dependent variables, and using an ANFIS model to perform prediction; 3, carrying out sequence stability test on the VMF except the trend term, and determining the order of AR and MA according to the correlation coefficient and the partial autocorrelation coefficient of the VMF; 4, using an ARIMA model to carry out time sequence prediction on the VMFs except the trend term; and 5, performing linear reconstruction on each VMD component prediction result to obtain a final power consumption demand prediction result.

Description

technical field [0001] The invention relates to the technical field of power data analysis, in particular to a monthly power demand forecasting method based on VMD-ANFIS-AR IMA. Background technique [0002] The power industry is an important energy pillar for social and economic take-off, advanced technology, and stable and convenient life. With the development of society and the continuous improvement of people's living standards, the demand for electric energy continues to increase. Good planning of future power grid construction and power production is an important guarantee for the continuous and rapid development of social and economic activities and the quality of life of residents. Power demand forecasting has always been an important topic in power systems, and it has important application values ​​in economic power generation, system security, management and planning. Therefore, it is extremely important for the planning and development of the power grid to use s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/06G06Q10/04G06N3/04G06N3/08
CPCG06Q50/06G06Q10/04G06N3/08G06N3/043
Inventor 董知周黄建平杨建华陈浩夏洪涛谢华森徐盛吴海峰郑文斌陈显辉蔡怡挺缪竞雄杨迁金烂聚徐海洋
Owner STATE GRID ZHEJIANG ELECTRIC POWER
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