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A two-stage short-term load forecasting approach using temperature daily profiles estimation

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Abstract

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

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Acknowledgements

We are grateful to Sonelgaz (Algeria’s national electricity and gas company) for providing 4 years of electricity data for this project.

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Authors and Affiliations

  1. Labged Laboratory, Computer Science Department, Badji Mokhtar University, PO Box 12, 23000, Annaba, Algeria

    Kheir Eddine Farfar & Mohamed Tarek Khadir

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  1. Kheir Eddine Farfar
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Correspondence to Kheir Eddine Farfar.

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Farfar, K.E., Khadir, M.T. A two-stage short-term load forecasting approach using temperature daily profiles estimation. Neural Comput & Applic 31, 3909–3919 (2019). https://doi.org/10.1007/s00521-017-3324-x

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  • DOI: https://doi.org/10.1007/s00521-017-3324-x

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