A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

<table class="table-group" id="tab6"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Indices</td><td class="align_center"><i>R</i><sup>2</sup></td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">GRU</td><td class="align_center">0.9748</td></tr><tr><td class="align_left">LSTM</td><td class="align_center">0.9805</td></tr><tr><td class="align_left">EEMD-LSTM</td><td class="align_center">0.9807</td></tr><tr><td class="align_left">PCA-EEMD-LSTM</td><td class="align_center">0.9955</td></tr><tr><td class="align_left">PROPOSED METHOD</td><td class="align_center">0.9961</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Results of <i>R</i><sup>2</sup> with the cross-validation dataset.</div>

Mathematical Problems in Engineering

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Table 6

Table 6: A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information 