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A comparison of modern deep neural network architectures for energy spot price forecasting

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  • F. Cordoni

    (University of Verona)

Abstract

The present paper is devoted to a comparison of modern deep learning architecture to forecast day—ahead electricity spot prices. The analysis focus on performances of four different neural network, namely a multilayer perceptron (MLP), a convolutional neural network (CNN), a long-short time memory (LSTM) network and a stacked CNN-LSTM network. We briefly review state-of-the-art neural networks architectures, highlighting main differences between the proposed methods. We show how the implemented deep learning methods outperforms other machine learning algorithms typically used in energy prices forecasting such as random forest (RF) and lasso. We compare the forecasts with different metrics, analysing also how results change with respect to day of the week and hour of the day.

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  • F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:3:d:10.1007_s42521-020-00022-2
    DOI: 10.1007/s42521-020-00022-2
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    2. Herry Kartika Gandhi & Ispány Márton, 2024. "Multi-step Natural Gas Price Forecasting using Ensemble Empirical Mode Decomposition and Long Short-Term Memory Hybrid Model," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 590-598, July.

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