Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

Sergey Voronin and Jarmo Partanen
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Sergey Voronin: LUT Energy, Laboratory of Electricity Markets and Power Systems, Lappeenranta University of Technology, P.O. Box 20, Lappeenranta 53851, Finland
Jarmo Partanen: LUT Energy, Laboratory of Electricity Markets and Power Systems, Lappeenranta University of Technology, P.O. Box 20, Lappeenranta 53851, Finland

Energies, 2013, vol. 6, issue 11, 1-24

Abstract: A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and nonlinear neural network models. The probability of a price spike occurrence is produced by a compound classifier in which three single classification techniques are used jointly to make a decision. Combined with the spike value prediction technique, the output from the price spike module aims to provide a comprehensive price spike forecast. The overall electricity price forecast is formed as combined normal price and price spike forecasts. The forecast accuracy of the proposed method is evaluated with real data from the Finnish Nord Pool Spot day-ahead energy market. The proposed method provides significant improvement in both normal price and price spike prediction accuracy compared with some of the most popular forecast techniques applied for case studies of energy markets.

Keywords: electricity price forecasts; price spike forecasts; compound classifier; hybrid methodology; input feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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