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Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

Author

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  • Vasileios Evangelopoulos

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Panagiotis Karafotis

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Pavlos Georgilakis

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece)

Abstract

The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

Suggested Citation

  • Vasileios Evangelopoulos & Panagiotis Karafotis & Pavlos Georgilakis, 2020. "Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method," Energies, MDPI, vol. 13(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4643-:d:410051
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    References listed on IDEAS

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    1. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    3. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    4. Nikolaos Koutsoukis & Pavlos Georgilakis, 2019. "A Chance-Constrained Multistage Planning Method for Active Distribution Networks," Energies, MDPI, vol. 12(21), pages 1-19, October.
    5. Rui Li & Wei Wang & Zhe Chen & Jiuchun Jiang & Weige Zhang, 2017. "A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches," Energies, MDPI, vol. 10(11), pages 1-27, October.
    6. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    7. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, July-Dece.
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    Cited by:

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