caa a22 4500 606196528 CHVBK 20210128100924.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10260-015-0313-z doi (NATIONALLICENCE)springer-10.1007/s10260-015-0313-z Multiple seasonal cycles forecasting model: the Italian electricity demand [Elektronische Daten] [Mauro Bernardi, Lea Petrella] Forecasting energy load demand data based on high frequency time series has become of primary importance for energy suppliers in nowadays competitive electricity markets. In this work, we model the time series of Italian electricity consumption from 2004 to 2014 using an exponential smoothing approach. Data are observed hourly showing strong seasonal patterns at different frequencies as well as some calendar effects. We combine a parsimonious model representation of the intraday and intraweek cycles with an additional seasonal term that captures the monthly variability of the series. Irregular days, such as public holidays, are modelled separately by adding a specific exponential smoothing seasonal term. An additive ARMA error term is then introduced to lower the volatility of the estimated trend component and the residuals' autocorrelation. The forecasting exercise demonstrates that the proposed model performs remarkably well, in terms of lower root mean squared error and mean absolute percentage error criteria, in both short term and medium term forecasting horizons. Springer-Verlag Berlin Heidelberg, 2015 Electricity demand forecasting nationallicence Exponential smoothing nationallicence Multiple seasonality nationallicence Single source of error models nationallicence Bernardi Mauro Department of Statistics, University of Padua, Padua, Italy aut Petrella Lea Department of MEMOTEF, Sapienza University of Rome, Via del Castro Laurenziano, 9, 00161, Rome, Italy aut Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 671-695 1618-2510 24:4<671 2015 24 10260 https://doi.org/10.1007/s10260-015-0313-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-015-0313-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bernardi Mauro Department of Statistics, University of Padua, Padua, Italy aut NATIONALLICENCE 700 1- Petrella Lea Department of MEMOTEF, Sapienza University of Rome, Via del Castro Laurenziano, 9, 00161, Rome, Italy aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 671-695 1618-2510 24:4<671 2015 24 10260