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Energy Load Forecasting: Investigating Mid-Term Predictions with Ensemble Learners

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Artificial Intelligence Applications and Innovations (AIAI 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 646))

Abstract

In the structure of the modern world, energy and especially electricity is a prerequisite for regularity. Thus, the requirement for accurate forecasts regarding power system loads seems self-evident. In machine learning, a time series forecasting endeavor can be treated as a regression problem. In such scenarios, ensemble methods are often used for robustness and increased accuracy of the generated predictions. This work is a comparative investigation of the use of ensemble schemes for medium-term forecasting of energy system load. The use of over 300 regression schemes is investigated, in a total of 8 different modifications of the input data, over 5 different time-frames, that is, one day, 7-day, 14-day, 21-day, and 30-day horizons, resulting in a loop of 12000 experiments. Summary tables with representative results from the corresponding Friedman rankings are presented.

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  1. Department of Mathematics, University of Patras, 265 04, Patras, Rion, Greece

    Charalampos M. Liapis, Aikaterini Karanikola & Sotiris Kotsiantis

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  2. Aikaterini Karanikola
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Correspondence to Charalampos M. Liapis .

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  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John Macintyre

  4. Universidade do Minho, Guimaraes, Portugal

    Paulo Cortez

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Liapis, C.M., Karanikola, A., Kotsiantis, S. (2022). Energy Load Forecasting: Investigating Mid-Term Predictions with Ensemble Learners. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-08333-4_28

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