[Forum SIS] seminar: Matteo Fasiolo "Generalized additive models for ensemble electricity demand forecasting", 25 May

[Umberto Picchini]

You are welcome to the next Statistics seminar at Dept of Mathematical 
Sciences at Chalmers and Göteborg University.

We are very glad to have, on Tuesday 25 May, Matteo Fasiolo 
<https://mfasiolo.github.io/> (Bristol University) who will talk on

*/Generalized additive models for ensemble electricity demand forecasting/*
*//*

Zoom:https://chalmers.zoom.us/j/65828735931
Password: 145171

When: 14.15-15.15 Swedish time,  25 May.

Feel free to circulate this invitation in your network.

*​Abstract *
*
Future grid management systems will coordinate distributed production 
and storage resources to manage, in a cost-effective fashion,
the increased load and variability brought by the electrification of 
transportation and by a higher share of weather-dependent production.
Electricity demand forecasts at a low level of aggregation will be key 
inputs for such systems. In this talk, I'll focus on forecasting demand 
at the individual household level,
which is more challenging than forecasting aggregate demand, due to the 
lower signal-to-noise ratio and to the heterogeneity of consumption 
patterns across households.
I'll describe a new ensemble method for probabilistic forecasting, which 
borrows strength across the households while accommodating their 
individual idiosyncrasies.
The first step consists of designing a set of models or 'experts' which 
capture different demand dynamics and fitting each of them to the data 
from each household.
Then the idea is to construct an aggregation of experts where the 
ensemble weights are estimated on the whole data set, the main 
innovation being that we let the weights vary with the covariates by 
adopting an additive model structure. In particular, the proposed 
aggregation method is an extension of regression stacking (Breiman, 
1996) where the mixture weights are modelled using linear combinations 
of parametric, smooth or random effects.
The methods for building and fitting additive stacking models are 
implemented by the gamFactory R package, available 
athttps://github.com/mfasiolo/gamFactory 
<https://github.com/mfasiolo/gamFactory>

About the speaker
Matteo Fasiolo **
Matteo Fasiolo is lecturer at the School of Mathematics at University of 
Bristol, UK. He is a statistician working on Generalized Additive Models 
(GAMs) and on developing new statistical methodology and software 
(mainly R packages) for tackling interesting scientific and industrial 
problems. The main application he is currently focusing on is 
electricity demand forecasting. Other research interests are intractable 
likelihoods and importance sampling.









*

-- 
_________________________________________________________
Umberto Picchini, Associate Professor, PhD, Docent
https://umbertopicchini.github.io/  , twitter: @uPicchini

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