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Regression Technique for Electricity Load Modeling and Outlined Data Points Explanation

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Advances in Soft and Hard Computing (ACS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 889))

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Abstract

Hundreds of explanatory variables such as historical consumption data, climate variables, socioeconomic and demographics parameters, etc. are used to forecast major forms of commercial energy consumptions. The scientists increasingly face the problems of big data: huge amount of data which grows in time. This article presents the analysis of the relationships between electricity amount to deliver and factors having more or less significant impact on electricity consumptions. The linear regression algorithm is used to reduce the set of explanatory variables and to evaluate their importance. A reduction of number of variables without significant loss of accuracy of the model is presented. Next, regression decision trees are used both to evaluate the quality of the modeled energy consumption, as well as to further explanatory variables reduction. The last part of the article deals with the outlining data points and explains reasons they come off model predictions.

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Author information

Authors and Affiliations

  1. Department of Informatics, SGGW, Nowoursynowska 159, 02-787, Warsaw, Poland

    Krzysztof Karpio, Piotr Łukasiewicz & Rafik Nafkha

Authors
  1. Krzysztof Karpio
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  2. Piotr Łukasiewicz
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  3. Rafik Nafkha
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Corresponding authors

Correspondence to Krzysztof Karpio , Piotr Łukasiewicz or Rafik Nafkha .

Editor information

Editors and Affiliations

  1. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Jerzy Pejaś

  2. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Imed El Fray

  3. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Tomasz Hyla

  4. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Karpio, K., Łukasiewicz, P., Nafkha, R. (2019). Regression Technique for Electricity Load Modeling and Outlined Data Points Explanation. In: Pejaś, J., El Fray, I., Hyla, T., Kacprzyk, J. (eds) Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-030-03314-9_5

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