Electricity Price Forecast for Futures Contracts with Artificial Neural Network and Spearman Data Correlation

  • João Nascimento
  • Tiago PintoEmail author
  • Zita Vale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)


Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.


Artificial neural networks Electricity price Forecasting Futures contracts Spearman correlation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Energia SimplesPortoPortugal
  2. 2.GECAD – Research GroupInstitute of Engineering, Polytechnic of Porto (ISEP/IPP)PortoPortugal

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