Exportar Publicação

Teodoro, M. F., Andrade, M., Silva, E. C., Borges, A. & Covas, R. (2018). Energy prices forecasting using GLM. In Teresa A. Oliveira, Christos P. Kitsos, Amílcar Oliveira, Luís Grilo (Ed.), Recent studies in risk analysis and statistical modeling.: Springer.

M. F. Teodoro et al.,  "Energy prices forecasting using GLM", in Recent studies in risk analysis and statistical modeling, Teresa A. Oliveira, Christos P. Kitsos, Amílcar Oliveira, Luís Grilo, Ed., Springer, 2018

@incollection{teodoro2018_1765949958162,
	author = "Teodoro, M. F. and Andrade, M. and Silva, E. C. and Borges, A. and Covas, R.",
	title = "Energy prices forecasting using GLM",
	chapter = "",
	booktitle = "Recent studies in risk analysis and statistical modeling",
	year = "2018",
	volume = "",
	series = "",
	edition = "",
	publisher = "Springer",
	address = "",
	url = "https://www.springer.com/gp/book/9783319766041"
}

TY  - CHAP
TI  - Energy prices forecasting using GLM
T2  - Recent studies in risk analysis and statistical modeling
AU  - Teodoro, M. F.
AU  - Andrade, M.
AU  - Silva, E. C.
AU  - Borges, A.
AU  - Covas, R.
PY  - 2018
SN  - 1431-1968
UR  - https://www.springer.com/gp/book/9783319766041
AB  - The work described in this article results from a problem proposed by the company EDP - Energy Solutions Operator, in the framework of ESGI 119th, European Study Group with Industry, during July 2016. Markets for electricity have two characteristics: the energy is mainly no-storable and volatile prices at exchanges are issues to take into consideration. These two features, between others, contribute significantly to the risk of a planning process. The aim of the problem is the short term forecast of hourly energy prices. In present work, GLM is considered a useful technique to obtain a predictive model where its predictive power is discussed. The results show that in the GLM framework the season of the year, month or winter/ summer period revealed significant explanatory variables in the different estimated models. The in-sample forecast is promising, conducting to adequate measures of performance.
ER  -