Short-term load forecasting using a two-stage sarimax model

Agostino Tarsitano and Ilaria L. Amerise

Energy, 2017, vol. 133, issue C, 108-114

Abstract: The primary aim of this study is to develop a new forecasting system for hourly electricity load in six Italian macro-regions. The statistical methodology is centered around a dynamic regression model in which important external predictors are included in a seasonal autoregressive integrate moving average process (sarimax). Specifically, the external variables are lagged hourly loads and calendar effects.

Keywords: Model building; Time series; Linear regression; External predictors (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217308848
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:133:y:2017:i:c:p:108-114

DOI: 10.1016/j.energy.2017.05.126

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().