基于集合经验模态分解和ARIMA-GRNN的负荷预测方法

doi:10.16180/j.cnki.issn1007-7820.2021.12.008

Abstract

Abstract:

In view of the problem that traditional load forecasting methods hardly take into account the inherent linear and nonlinear characteristics of power load, this study proposes a hybrid load forecasting model based on EEMD and ARIMA-GRNN. In this method, EEMD method is used to decompose the load data into IMF components and residual terms without modal aliasing. ARIMA model algorithm is used to make linear prediction for each IMF component to obtain the time series prediction component, and subtract it from the original data to obtain the non-linear component. The non-linear component is predicted by the GRNN neural network algorithm to obtain the predicted value of the non-linear component. The obtained linear and the nonlinear predicted components are added to obtain the final predicted value. The simulation results show that the hybrid forecasting model based on EEMD and ARIMA-GRNN proposed in the present study is superior to the load forecasting method using a single algorithm in terms of forecasting accuracy and performance.

Key words: load forecasting, EEMD, ARIMA-GRNN, hybrid model, IMF, neural network algorithm, nonlinear, time series

CLC Number:

TP13

WANG Hongliang,CHEN Xinyuan,ZHAO Yumeng. Load Forecasting Method Based on Ensemble Empirical Mode Decomposition and ARIMA-GRNN[J].Electronic Science and Technology, 2021, 34(12): 42-48.

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References 19

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预测算法	均方差误差	平均绝对误差	平均绝对百分误差/%
ARIMA	482.296 0	366.669 1	7.567 5
GRNN	1 110.241 8	793.501 7	18.048 6
ARIMA-GRNN	453.438 1	291.757 7	6.399 2
EEMD-ARIMA-GRNN	394.327 8	266.089 6	5.564 2

Load Forecasting Method Based on Ensemble Empirical Mode Decomposition and ARIMA-GRNN

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