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Deep Learning Based Load Forecasting with Decomposition and Feature Selection Techniques

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Title Deep Learning Based Load Forecasting with Decomposition and Feature Selection Techniques
 
Creator Subbiah, Siva Sankari
P, Senthil Kumar
 
Subject Boruta feature selection
Electricity load prediction
Ensemble empirical mode decomposition
Gated recurrent unit
Recurrent neural network
 
Description 505-517
The forecasting of short term electricity load plays a vital role in power system. It is essential for the power system's
reliable, secure, and cost-effective functioning. This paper contributes significantly for enhancing the accuracy of short term
electricity load forecasting. It presents a hybrid forecasting model called Gated Recurrent Unit with Ensemble Empirical
Mode Decomposition and Boruta feature selection (EBGRU). It is a hybrid model that addresses the non-stationary,
non-linearity and noisy issues of the time series input by using Ensemble Empirical Mode Decomposition (EEMD). It also
addresses overfitting and curse of dimensionality issues of load forecasting by identifying the pertinent features using Boruta
wrapper feature selection. It effectively handles the uncertainty and temporal dependency characteristics of load and forecasts
the future load using deep learning based Gated Recurrent Unit (GRU). The proposed EBGRU model is experimented by using
European and Australian Electricity load datasets. The temperature has high correlation with load demand. In this study, both
load and temperature features are considered for the accurate short term load forecasting. The experimental outcome
demonstrates that the proposed EBGRU model outperforms other deep learning models such as RNN, LSTM, GRU, RNN with
EEMD and Boruta (EBRNN) and LSTM with EEMD and Boruta (EBLSTM).
 
Date 2022-05-05T10:48:34Z
2022-05-05T10:48:34Z
2022-05
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/59675
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(05) [May 2022]