AUTHORS: Ana Borges, Eliana Costa E Silva, Ricardo Covas
Download as PDF
ABSTRACT: We propose to contribute to the problematic of Electricity Price Forecasting with a longitudinal statistical approach. We focus our interest on forecasting intra-day prices using hourly data (disaggregated data) in a multivariate approach rather than in the usually used univariate approach, by adjusting a mixed-effects longitudinal model to the Iberian Electricity Market hourly prices from January 1th 2015 to June 26th 2016, in a total of 13 032 observations. Results indicate that a longitudinal approach considering a mixed-effects model, with month and weekday as fixed effects, hour group as random effect and an AutoRegressive component of order 7 describing the within hour dependence, yield a model that explains the intra-day and intra-hour dynamics for the electricity hourly prices.
KEYWORDS: Electricity Price Forecasting, Longitudinal Mixed-effects Model, MIBEL
REFERENCES:
[1] Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd ed., Holden-Day, San Francisco.
[2] Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J. (2003) ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 10141020.
[3] Costa e Silva, E., Borges, A., Teodoro, M.F., Andrade, M.A.P., Covas, R. (2017) Time Series Data Mining for Energy Prices Forecasting: An Application to Real Data. IN A.M. Madureira et al. (Eds.), Intelligent Systems Design and Applications, Advances in Intelligent Systems and Computing 557.
[4] Diggle, P., Heagerty, P., Liang, K.Y., Zeger, S. (2002). Analysis of Longitudinal Data, 2nd edition. Oxford, England: Oxford University Press.
[5] dos Santos Coelho, L., Santos, A. A. (2011). A RBF neural network model with GARCH errors: application to electricity price forecasting. Electric Power Systems Research, 81(1), 74-83.
[6] Huisman, R., Huurman, C., Mahieu, R. (2007). Hourly electricity prices in day-ahead markets. Energy Economics, 29(2), 240-248.
[7] Klaboe, G. (2015). Forecasting hourly electricity prices for bidding optimization. In European Energy Market (EEM), 2015 12th International Conference on the IEEE,1-5.
[8] Pinheiro, J. C., Bates, D. M. (2000). Mixedeffects models in S and S-PLUS. New York: Springer.
[9] Weron, R. (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081.
[10] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2016.
[11] Pinheiro J, Bates D, DebRoy S, Sarkar D and R Core Team, (2014). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1- 118, http://CRAN.R-project.org/package=nlme.
[12] Frees, E.W. and Miller, T.W. (2004). Sales forecasting using longitudinal data models. International Journal of Forecasting 20, 99114.
[13] Monteiro, C., Ramirez-Rosado, I. J., FernandezJimenez, L. A., and Conde, P. (2016). ShortTerm Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market. Energies, 9(9), 721.
[14] Munoz, M.P., Corchero, C., Heredia, F.J. (2013). ˜ Improving electricity market price forecasting with factor models for the optimal generation bid. Int. Stat. Rev. 81(2), 289306
[15] Osorio, G.J., Goncalves, J., Lujano-Rojas, J., ´ Catalao, J. (2016). Enhanced forecasting approach for electricity market prices and wind power data series in the shortterm. Energies 9(693), 119.
[16] Murthy, G.G.P., Sedidi, V., Panda, A.K., Rath, B.N. (2014). Forecasting electricity prices in deregulated wholesale spot electricity market - a review. Int. J. Energy Econ. Policy 4(1), 32.
