**ELECTRICITY
DEMAND FORECASTS USING GENERALIZED EXPONENTIAL SMOOTHING MODELS **

José D. Bermúdez, Ana Corberán-Vallet,
José V. Segura, Enriqueta Vercher

**Abstract**

We introduce
an extension of exponential smoothing to deal with covariates and double seasonality
that could easily be adapted to more than two seasonal cycles. Assuming
additive effects and a stochastic component given by independent, homoscedastic normal errors, the exponential smoothing
model can be expressed as an equivalent linear dynamic model with a very
peculiar structure of the covariance matrix. The covariance matrix is a
function of the unknown smoothing parameters only, while the mean vector only
depends on the unknown initial conditions. These facts allow for a
simplification of the statistical analysis of the model. Following the Bayesian
paradigm, we obtain the joint posterior distribution of all the unknowns. Only
the marginal posterior of the smoothing parameters is analytically intractable
and has to be approached using simulation techniques. The conditional
distribution of initial conditions giving the smoothing parameters is
well-known and it can be integrated out exactly in order to compute the
predictive distribution. Finally, we propose to integrate out the smoothing parameters
using Monte-Carlo techniques, obtaining an estimate of the predictive
distribution as well as their main characteristics: point forecasts and
prediction intervals. We apply this methodology to electricity demand forecasts
using two real data sets.

Lecture Notes in Management Science (2011) Vol. 3: 33-38

3rd International Conference on Applied Operational Research, Proceedings

© Tadbir Operational Research Group Ltd. All rights reserved.

www.tadbir.ca

ISSN 2008-0050 (Print)

ISSN 1927-0097 (Online)

**ARTICLE OUTLINE**

·
**Introduction **

·
**Bayesian Analysis Of The Innovations State Space Model **

·
**Applications To Electricity Demand Forecasting **

·
**References**