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Export Reference (APA) 
Mendes, D. A. & Mendes, V. (2014). Forecasting the Iberian Electricity Market Demand by using Nonlinear Time Series Tools. International Interdisciplinary Business-Economics Advancement Conference  (IIBA 2014).
Export Reference (IEEE) 
D. E. Mendes and V. M. Mendes,  "Forecasting the Iberian Electricity Market Demand by using Nonlinear Time Series Tools", in Int. Interdisciplinary Business-Economics Advancement Conf.  (IIBA 2014), Istanbul, 2014
Export BibTeX 
@misc{mendes2014_1765612980076,
	author = "Mendes, D. A. and Mendes, V.",
	title = "Forecasting the Iberian Electricity Market Demand by using Nonlinear Time Series Tools",
	year = "2014",
	howpublished = "Other",
	url = "http://istanbul2014.iibaconference.org/"
}
Export RIS 
TY  - CPAPER
TI  - Forecasting the Iberian Electricity Market Demand by using Nonlinear Time Series Tools
T2  - International Interdisciplinary Business-Economics Advancement Conference  (IIBA 2014)
AU  - Mendes, D. A.
AU  - Mendes, V.
PY  - 2014
CY  - Istanbul
UR  - http://istanbul2014.iibaconference.org/
AB  - With the paradigm shift regarding power systems, that used to be regulated and started to be liberalized, the study and forecast of prices and electricity loads have become a new topic of interest to researchers. Due to the peculiarities of electricity, electricity markets have very specific rules that must be understood before starting their study.
    This paper presents a study of the Iberian Electricity Market, represented by the series of loads, in the framework of nonlinear deterministic chaos. The main goal was to verify that the series of loads has chaotic features, reconstructing its attractor and estimating some invariants of the system as the correlation dimension, the Kolmogorov-Sinai entropy and the Lyapunov exponents. The forecast for the next 24 hours can then be done using deterministic tools like the method of time delay and artificial neural networks.
    As a result of this research, we identified evidence that the series of the loads is governed by a chaotic dynamical system and its predictions were successfully achieved.

ER  -