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<p>Dear all,</p>
<p>On Thursday (14 November) at 2pm in <span class="event-where">54 / 10037 (10B), we have an S3RI seminar from
</span><span class="event-where"><span class="event-description">Matt<wbr>eo Fasiolo (University of Bristol)</span> on
</span><span class="event-where"><span class="event-description">"</span></span><span class="event-where"><span class="event-description"><span class="event-description"><span class="event-description">Calibrated additive quantile regression for short-term
electricity demand forecasting</span></span>". Details are given below.<br>
</span></span></p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">The seminar will also be available via a live web-cast at</span></span></span></span><br>
<span class="event-description"><a class="moz-txt-link-freetext" href="https://southampton.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=87cc7058-7742-472a-a423-ab020087d032">https://southampton.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=87cc7058-7742-472a-a423-ab020087d032</a></span><br>
</p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">The talk will be followed by tea and cake in the staff reading room on level 4 of building 54. </span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span><span class="event-description">All are welcome!</span></span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Best wishes,</span></span></span></p>
<p><span class="event-where"><span class="event-description"><span>Helen</span></span></span></p>
<span class="event-details-label"></span><span class="event-description">Calibrated additive quantile regression for short-term electricity demand forecasting<br>
<br>
Matt<wbr>eo Fasiolo, University of Bristol<br>
<br>
In the electricity industry, short-term demand forecasts are an essential input for operations such as energy trading, production planning and transmission grid management. Generalized additive models (GAMs) have proved successful in this context, because they
offer competitive predictive accuracy while retaining interpretabilit<wbr>y<span>.
</span>A major limitation of GAMs is the parametric assumption on the distribution of the response, hence this talk focusses on more flexible non-parametric quantile regression GAM (QGAM) models. In particular, we show how QGAMs can be fitted in an empirical
Bayesian framework, aimed at providing good predictive performance and calibrated credible intervals, at a moderate computational cost. Depending on time, we might also discuss the implementation of the new methods in the qgam R package.<br>
</span><br>
<br>
<span class="event-where"><span class="event-description"><span><span class="event-description"><span><span class="event-description"></span><span class="event-description"><span class="event-description">For the current schedule of S3RI seminars, see
<a class="moz-txt-link-freetext" href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftinyurl.com%2Fs3riseminar&data=01%7C01%7C%7C85fad7ff54d04deb19fe08d766800d5f%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=dIlQ%2FygN4VS73RZt0RIrRUZwMwU0oJSOFFWyxCkQQ8g%3D&reserved=0" originalsrc="https://tinyurl.com/s3riseminar" shash="oyzWwe2EdQExvHi/h3GVGN0DM9Qfj8mt8a7j40ElPe8QADZywcrHEy8b68/JzSzUPEHM8qpFgnNmcmySgVuQv1XGvRvA7CjdLCCD+3Cp55h3XngQqxwI+xoVZhe3irqQYhiAuU1lABcmudej+9IHMez3KMwZ/Kb5IDNAAFlotP4=">
https://tinyurl.com/s3riseminar</a> </span></span></span></span></span></span></span><br>
<p><span class="event-where"><span class="event-description"><span><span class="event-description"></span></span></span></span></p>
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