Short Term Electricity Price Forecasting Through Convolutional Neural Network (CNN)

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1150)


High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict the EPF. Secondly, a principle component analysis model is used for the feature extraction. We have conducted simulations to prove the effectiveness of the proposed approach, which show that CNN based approach outperforms the multilayer perceptron model.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer Information ScienceHigher Colleges of TechnologyFujairahUnited Arab Emirates
  2. 2.COMSATS University IslamabadIslamabadPakistan

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