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Silva, E. C. e., Borges, A., Teodoro, M. F., Andrade, M. A. P. & Covas, R. (2016). Time series data mining for energy prices forecasting: an application to real data. In Madureira, A. M., Abraham, A., Gamboa, D., and Novais, P. (Ed.), Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing. (pp. 649-658). Porto: Springer.

E. C. Silva et al.,  "Time series data mining for energy prices forecasting: an application to real data", in Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing, Madureira, A. M., Abraham, A., Gamboa, D., and Novais, P., Ed., Porto, Springer, 2016, vol. 557, pp. 649-658

@inproceedings{silva2016_1768680779414,
	author = "Silva, E. C. e. and Borges, A. and Teodoro, M. F. and Andrade, M. A. P. and Covas, R.",
	title = "Time series data mining for energy prices forecasting: an application to real data",
	booktitle = "Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing",
	year = "2016",
	editor = "Madureira, A. M., Abraham, A., Gamboa, D., and Novais, P.",
	volume = "557",
	number = "",
	series = "",
	doi = "10.1007/978-3-319-53480-0_64",
	pages = "649-658",
	publisher = "Springer",
	address = "Porto",
	organization = "Machine Intelligence Research Labs (MIR Labs)",
	url = "https://link.springer.com/chapter/10.1007/978-3-319-53480-0_64"
}

TY  - CPAPER
TI  - Time series data mining for energy prices forecasting: an application to real data
T2  - Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing
VL  - 557
AU  - Silva, E. C. e.
AU  - Borges, A.
AU  - Teodoro, M. F.
AU  - Andrade, M. A. P.
AU  - Covas, R.
PY  - 2016
SP  - 649-658
SN  - 2194-5357
DO  - 10.1007/978-3-319-53480-0_64
CY  - Porto
UR  - https://link.springer.com/chapter/10.1007/978-3-319-53480-0_64
AB  - Recently, at the 119th European Study Group with Industry, the Energy Solutions Operator EDP proposed a challenge concerning electricity prices simulation, not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. The main purpose was short-term Electricity Price Forecasting (EPF). This analysis is contextualized in the study of time series behavior, in particular multivariate time series, which is considered one of the current challenges in data mining. In this work a short-term EPF analysis making use of vector autoregressive models (VAR) with exogenous variables is proposed. The results show that the multivariate approach using VAR, with the season of the year and the type of day as exogenous variables, yield a model that explains the intra-day and intra-hour dynamics of the hourly prices.
ER  -