A Short-Term Photovoltaic Power Generation Forecast Method Based on LSTM

<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">Meteorological parameters</td><td class="align_center">Pearson’s correlation coefficient</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Temperature</td><td class="align_center">0.2589</td></tr><tr><td class="align_left">Humidity</td><td class="align_center">−0.3686</td></tr><tr><td class="align_left">Wind speed</td><td class="align_center">0.1915</td></tr><tr><td class="align_left">Weather</td><td class="align_center">0.0114</td></tr><tr><td class="align_left">Wind direction</td><td class="align_center">0.1657</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Pearson’s correlation coefficient between meteorological data and PV power generation.</div>

Mathematical Problems in Engineering

tab2

Table 2

Table 2: A Short-Term Photovoltaic Power Generation Forecast Method Based on LSTM 