Electricity Price Forecasting

2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia

Electricity price forecasting – ARIMA model approach
Tina Jakaša #1, Ivan Andročec #2, Petar Sprčić *3
Hrvatska elektroprivreda Ulica grada Vukovara 37, Zagreb, Croatia
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[e-mail] [e-mail]

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* HEP Trade Ulica grada Vukovara 37, Zagreb, Croatia
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[e-mail]

Abstract— Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze and forecast time series. The model is applied to time series consisting of day-ahead electricity prices from EPEX power exchange.

II. CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING CRISP-DM is a commonly used standard that describes a life cycle of a data mining process 3 . The life cycle consists of six phases, as shown in Fig.1.

I. INTRODUCTION Electricity is among the most volatile of commodities. Daily average change of the spot electricity price can be up to 50 %, while at the same time for other commodities is up to 5 %. There are many market players depending on electricity price trends, such as generators, traders, suppliers and end customers (particularly large industrial customers). Clearly it is very important for them to have accurate forecasting models for electricity prices. The paper focuses on forecasting day-ahead electricity prices using European Energy Exchange data as the reference power market. EEX cooperates with the French Powernext SA. EEX holds 50% of the shares in the joint venture EPEX Spot SE based in Paris which operates short-term trading in power – the so-called Spot Market – for Germany, France, Austria and

References: [1] The EEX Website [Online]. Available: http://www.eex.com/en/EEX [2] P. Sprcic, S. Krajcar, The application of Derivatives…, Energija, vol. 56(2007), no.4,pp.432-455 [3] P.Chapman , J. Clinton, R. Kerber, T. khabaza, T.Reinartz, C. Shearer and R. Wirth, CRISP-DM 1.0, Step-by-step data mining guide. [Online]. Available: http://www.crisp-dm.org/CRISPWP-0800.pdf / [4] R. Yaffee, Introduction to Time Series Analysis and Forecasting , Academic Press,NY , 1999. [5] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. 1994. Time series analysis: Forecasting and control, 3rd ed. Englewood Cliffs, N.J.: Prentice Hall. [6] The SPSS Website [Online]. Available: http://www.spss.com [7] S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ultrathin elevated channel low-temperature poly-Si TFT,” IEEE Electron Device Lett., vol. 20, pp. 569–571, Nov. 1999. [8] J. Contreras, R. Espinola, F.J. Nogales and A. J. Conejo, ARIMA Models to Predict Next-Day Electricity Prices, IEEE Transactions on Power Systems, vol.18, no.3, August 2003. Fig. 5. The observed, fit and forecast values for the German electricity market for period 2000-2011. The forecasted values are presented in fig. 2. B. Estimated parameter values for best fitted model There are several techniques to estimate model parameters such us conditional lease squares, but SPSS employs maximum likelihood for model estimation. Estimated values form EPEX market is presented in table III for best fitted ARIMA model. TABLE III ESTIMATED PARAMETER VALUES OF THE EPEX MARKET ARIMA MODEL 225