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Marin Cerjan; Marin Matijaš; Marko Delimar (2014)
Publisher: Multidisciplinary Digital Publishing Institute
Journal: Energies
Types: Article
Subjects: electricity price, data mining, short term electricity price forecasting, Technology, neural network, forecasting techniques, T, data mining; neural network; price volatility; short term electricity price forecasting; forecasting techniques; spot market; electricity price, price volatility, spot market
jel: jel:Q0, jel:Q, jel:Q4, jel:Q47, jel:Q49, jel:Q48, jel:Q43, jel:Q42, jel:Q41, jel:Q40
Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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