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<p>Deep Learning based Load Forecasting with Decomposition and Feature Selection Techniques</p>

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Title Statement <p>Deep Learning based Load Forecasting with Decomposition and Feature Selection Techniques</p>
 
Added Entry - Uncontrolled Name Subbiah, Siva Sankari ; Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor 517 127, Andhra Pradesh, India
Senthil Kumar, Paramasivan ; School of Information Technology & Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India
 
Uncontrolled Index Term Boruta feature selection, Electricity load prediction, Ensemble empirical mode decomposition, Gated recurrent unit, Recurrent neural network
 
Summary, etc. <p class="Abstract">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).<strong></strong></p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2022-06-01 22:30:46
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/56794
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 81, ##issue.no## 05 (2022): Journal of Scientific and Industrial Research
 
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Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/56794/465584988
http://op.niscair.res.in/index.php/JSIR/article/download/56794/465584989
http://op.niscair.res.in/index.php/JSIR/article/download/56794/465584990