Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Feature</td><td class="align_center">Correlation coefficient</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Dry-bulb temperature</td><td class="align_center">0.0765</td></tr><tr><td class="align_left">Dew point temperature</td><td class="align_center">−0.1041</td></tr><tr><td class="align_left">Wet-bulb temperature</td><td class="align_center">−0.0272</td></tr><tr><td class="align_left">Humidity</td><td class="align_center">−0.1972</td></tr><tr><td class="align_left">Electricity price</td><td class="align_center">0.1946</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Correlation coefficient between each feature and historical load.</div>

Mathematical Problems in Engineering

tab2

Table 2

Table 2: Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors 