ttu
/
CaReCur
mirror of https://github.com/thomastu/CaReCur


  
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          <span>California Renewable Curtailments  documentation</span></a></h1>
        <h2 class="heading"><span>Conclusion</span></h2>
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  <div class="section" id="conclusion">
<h1>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">¶</a></h1>
<p>A reliable statistical model wasn’t found.</p>
<div class="section" id="next-steps">
<h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2>
<p>Two main areas of work were not explored further due to time and computing constraints:</p>
<p><strong>Data Engineering</strong></p>
<dl class="simple">
<dt>GFS Spatial resolution</dt><dd><p>The GFS data used was at a 1° spatial resolution.  A higher granularity of data would provide a more accurate representation of weather effects.</p>
</dd>
<dt>Expanded Analysis Period</dt><dd><p>The initial exploratory data analysis suggested a rough seasonality to</p>
</dd>
</dl>
<p><strong>Feature Engineering</strong></p>
<dl class="simple">
<dt>Isolating Demand-Side Drivers</dt><dd><p>The curtailment problem could also be described from a demand-side perspective.  During periods of depressed or low demand (e.g. days with low air conditioning usage), would exacerbate the curtailment quantity.  In this study, a historic load from CAISO was used (though in principle, a day ahead forecast could be dropped in as well.)</p>
</dd>
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<p>Additional data engineering work could benefit the predictive capabilities of the approaches laid out in this study.  Of note, historic weather forecasts at a higher resolution</p>
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