4.7 Article

Spot price forecasting for best trading strategy decision support in the Iberian electricity market

Journal

EXPERT SYSTEMS WITH APPLICATIONS
Volume 224, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.120059

Keywords

LSTM; Spot prices forecast; Futures prices; Electricity market; Risk premium; Trading strategy

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The paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis. The forecast model uses an Artificial Neural Network trained with the Long Short Term Memory architecture. Statistical analysis verifies the correlation and dependency between variables and the results indicate benefits for traders adopting the proposed trading strategy.
The increasing volatility in electricity markets has reinforced the need for better trading strategies by both sellers and buyers to limit the exposure to losses. Accordingly, this paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis based on this forecast. This strategy can help traders (buyers and sellers) decide whether to trade in the futures market (of varying monthly maturity) or to wait and trade in the spot market. The forecast model consists of an Artificial Neural Network trained with the Long Short Term Memory architecture to predict the average monthly spot prices, using only market price -related data as input variables. Statistical analysis verified the correlation and dependency between variables. The forecast model was trained, validated and tested with price data from the Iberian Electricity Market (MIBEL), in particular the Spanish zone, between January 2015 and August 2019. The last year of this period was reserved for testing the performance of the proposed forecast model and trading strategy. For comparison purposes, the results of a forecasting model trained with the Extreme Learning Machine over the same period are also pre-sented. In addition, the forecasted value of the average monthly spot price was used to perform a risk premium analysis. The results were promising, as they indicated benefits for traders adopting the proposed trading strategy, proving the potential of the forecast model and the risk premium analysis based on this forecast.

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