Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection
Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decom...
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Online Access: | https://www.mdpi.com/1996-1073/12/6/1140 |
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doaj-73b7befe02b7454f85807c01b5cd04f02020-11-24T21:21:35ZengMDPI AGEnergies1996-10732019-03-01126114010.3390/en12061140en12061140Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature SelectionXin Gao0Xiaobing Li1Bing Zhao2Weijia Ji3Xiao Jing4Yang He5School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaMany factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.https://www.mdpi.com/1996-1073/12/6/1140short-term electricity load forecastingEMDcorrelation analysisGRU |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xin Gao Xiaobing Li Bing Zhao Weijia Ji Xiao Jing Yang He |
spellingShingle |
Xin Gao Xiaobing Li Bing Zhao Weijia Ji Xiao Jing Yang He Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection Energies short-term electricity load forecasting EMD correlation analysis GRU |
author_facet |
Xin Gao Xiaobing Li Bing Zhao Weijia Ji Xiao Jing Yang He |
author_sort |
Xin Gao |
title |
Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection |
title_short |
Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection |
title_full |
Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection |
title_fullStr |
Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection |
title_full_unstemmed |
Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection |
title_sort |
short-term electricity load forecasting model based on emd-gru with feature selection |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2019-03-01 |
description |
Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher. |
topic |
short-term electricity load forecasting EMD correlation analysis GRU |
url |
https://www.mdpi.com/1996-1073/12/6/1140 |
work_keys_str_mv |
AT xingao shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection AT xiaobingli shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection AT bingzhao shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection AT weijiaji shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection AT xiaojing shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection AT yanghe shorttermelectricityloadforecastingmodelbasedonemdgruwithfeatureselection |
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1725999189706407936 |