A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left" rowspan="2">Parameter</td><td class="align_center" colspan="2">Voltage</td><td class="align_center" rowspan="2">Operating current</td><td class="align_center" rowspan="2">Microcontroller</td></tr><tr class="thead"><td class="align_center">Operating</td><td class="align_center">Recommended</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Rating</td><td class="align_center">5 V</td><td class="align_center">5 – 12 V</td><td class="align_center">0.04 A</td><td class="align_center">Built-in ATmega328</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Specifications of Arduino Uno.</div>

International Journal of Photoenergy

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Table 4

Table 4: A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms 