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X-model: further development and possible modifications

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  • Sergei Kulakov

Abstract

Despite its critical importance, the famous X-model elaborated by Ziel and Steinert (2016) has neither bin been widely studied nor further developed. And yet, the possibilities to improve the model are as numerous as the fields it can be applied to. The present paper takes advantage of a technique proposed by Coulon et al. (2014) to enhance the X-model. Instead of using the wholesale supply and demand curves as inputs for the model, we rely on the transformed versions of these curves with a perfectly inelastic demand. As a result, computational requirements of our X-model reduce and its forecasting power increases substantially. Moreover, our X-model becomes more robust towards outliers present in the initial auction curves data.

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  • Sergei Kulakov, 2019. "X-model: further development and possible modifications," Papers 1907.09206, arXiv.org.
    • Handle: RePEc:arx:papers:1907.09206
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    References listed on IDEAS

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    1. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    2. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    3. Bartosz Uniejewski & Rafal Weron & Florian Ziel, 2017. "Variance stabilizing transformations for electricity spot price forecasting," HSC Research Reports HSC/17/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    5. Sergei Kulakov & Florian Ziel, 2019. "The Impact of Renewable Energy Forecasts on Intraday Electricity Prices," Papers 1903.09641, arXiv.org, revised Nov 2019.
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    8. Sergei Kulakov & Florian Ziel, 2019. "Determining Fundamental Supply and Demand Curves in a Wholesale Electricity Market," Papers 1903.11383, arXiv.org, revised Nov 2019.
    9. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    10. Spiecker, Stephan & Weber, Christoph, 2014. "The future of the European electricity system and the impact of fluctuating renewable energy – A scenario analysis," Energy Policy, Elsevier, vol. 65(C), pages 185-197.
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    Cited by:

    1. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    2. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.

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