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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
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.
Exportar Referência (IEEE)
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
Exportar BibTeX
@incollection{teodoro2018_1714293138981,
	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"
}
Exportar RIS
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  -