Novel approaches to the energy load unbalance forecasting in the Italian electricity market View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Luca Di Persio, Alessandro Cecchin, Francesco Cordoni

ABSTRACT

In the present paper we study the statistical properties of the Italian daily electricity load market, by mean of different statistical methods, such, e.g., the exponential smoothing model, the ARMA-ARIMA model and the ARIMA-GARCH model, also providing results about the goodness of each of the proposed approaches. Moreover, we show how the aforementioned models behave if exogenous regressors, as the day of the week or the temperature, are additionally taken into account. Analysed methods are then exploited to perform the one-day ahead energy load prediction, where the main focus is on guessing the right sign of the energy load unbalance. More... »

PAGES

5

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URI

http://scigraph.springernature.com/pub.10.1186/s13362-017-0035-y

DOI

http://dx.doi.org/10.1186/s13362-017-0035-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1083906766


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