UKload

Dataset on UK electricity demand, taken from the national grid (<a href="https://www.nationalgrid.com/">https://www.nationalgrid.com/</a>).

Smooth additive quantile regression models, fitted using
the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
'quantreg', the smoothing parameters are estimated automatically by marginal
loss minimization, while the regression coefficients are estimated using either
PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
credible intervals of the estimated effects have approximately the correct
coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
fits non-parametric quantile regression models.

Matteo Fasiolo

qgam

Smooth Additive Quantile Regression Models

Ben Griffiths

Simon N. Wood

Margaux Zaffran

Yannig Goude

Raphael Nedellec

UKload function

<code>UKload</code> contains the following variables:<dl>
<dt>NetDemand</dt>
<dd>net electricity demand between 11:30am and 12am.</dd><dt>wM</dt>
<dd>instantaneous temperature, averaged over several English cities.</dd><dt>wM_s95</dt>
<dd>exponential smooth of <code>wM</code>, that is <code>wM_s95[i] = a*wM_s95[i-1] + (1-a)*wM[i]</code> with <code>a=0.95</code></dd>
.
<dt>Posan</dt>
<dd>periodic index in <code>[0, 1]</code> indicating the position along the year.</dd><dt>Dow</dt>
<dd>factor variable indicating the day of the week.</dd><dt>Trend</dt>
<dd>progressive counter, useful for defining the long term trend.</dd><dt>NetDemand.48</dt>
<dd>lagged version of <code>NetDemand</code>, that is <code>NetDemand.48[i] = NetDemand[i-2]</code>.</dd><dt>Holy</dt>
<dd>binary variable indicating holidays.</dd><dt>Year</dt>
<dd>should be obvious.</dd><dt>Date</dt>
<dd>should be obvious.</dd>
</dl>

Format

Dataset on UK electricity demand, taken from the national grid (<a href='https://www.nationalgrid.com/'>https://www.nationalgrid.com/</a>).

UK electricity load data — UKload

<code>UKload</code> contains the following variables:<dl>
<dt>NetDemand</dt>
<dd>net electricity demand between 11:30am and 12am.</dd>

<dt>wM</dt>
<dd>instantaneous temperature, averaged over several English cities.</dd>

<dt>wM_s95</dt>
<dd>exponential smooth of <code>wM</code>, that is <code>wM_s95[i] = a*wM_s95[i-1] + (1-a)*wM[i]</code> with <code>a=0.95</code></dd>
.
<dt>Posan</dt>
<dd>periodic index in <code>[0, 1]</code> indicating the position along the year.</dd>

<dt>Dow</dt>
<dd>factor variable indicating the day of the week.</dd>

<dt>Trend</dt>
<dd>progressive counter, useful for defining the long term trend.</dd>

<dt>NetDemand.48</dt>
<dd>lagged version of <code>NetDemand</code>, that is <code>NetDemand.48[i] = NetDemand[i-2]</code>.</dd>

<dt>Holy</dt>
<dd>binary variable indicating holidays.</dd>

<dt>Year</dt>
<dd>should be obvious.</dd>

<dt>Date</dt>
<dd>should be obvious.</dd>


</dl>

UKload: UK electricity load data

Description

Usage

Arguments

Value

Format

Details

References

Examples