Regression Technique for Electricity Load Modeling and Outlined Data Points Explanation

  • Krzysztof KarpioEmail author
  • Piotr ŁukasiewiczEmail author
  • Rafik NafkhaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 889)


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.


Electricity load modeling Regression decision tree Electricity short-term forecasting 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, SGGWWarsawPoland

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