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Short-Term Electricity Price Forecasting: Deep ANN vs GAM

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Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1175))

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Abstract

Examining the spot price series of electricity over the course of time, it is striking that the electricity price across the day takes a course that is determined by power consumption following a day and night rhythm. The daily course changes in its height and temporal extent in both, the course of the week, as well as with the course of the year. This study deals methodologically with this intra-day and seasonal behaviour. We compare the forecasting accuracy of Deep Artificial Neural Nets (ANN) of different architectures and Generalized Additive Models (GAM) and apply these models with European data.

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Authors and Affiliations

  1. Kiel University of Applied Sciences, Sokratesplatz 2, 24149, Kiel, Germany

    Jan-Hendrik Meier, Stephan Schneider, Chan Le & Iwana Schmidt

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  1. Jan-Hendrik Meier
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  2. Stephan Schneider
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  3. Chan Le
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  4. Iwana Schmidt
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Correspondence to Jan-Hendrik Meier .

Editor information

Editors and Affiliations

  1. Zaporizhzhia National University, Zaporizhzhia, Ukraine

    Vadim Ermolayev

  2. Université Cote d’Azur, Nice, France

    Frédéric Mallet

  3. University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

    Vitaliy Yakovyna

  4. University of Klagenfurt, Klagenfurt am Wörthersee, Austria

    Heinrich C. Mayr

  5. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

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Meier, JH., Schneider, S., Le, C., Schmidt, I. (2020). Short-Term Electricity Price Forecasting: Deep ANN vs GAM. In: Ermolayev, V., Mallet, F., Yakovyna, V., Mayr, H., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2019. Communications in Computer and Information Science, vol 1175. Springer, Cham. https://doi.org/10.1007/978-3-030-39459-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-39459-2_12

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