Analysing the determinants of the Turkish household electricity consumption using gradient boosting regression tree

Denizhan Guven*, M. Ozgur Kayalica

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Residential buildings are the second largest electricity consumer in Turkey. Thus, the goal here is to detect the factors that determine the electricity consumption of the households in Turkey using the Household Budget Survey (HBS). This study applies Decision Tree (DT), Random Forest (RF) and Gradient Boosted Regression Tree (GBRT) methods. Since the GBRT method provides the lowest Root Mean Squared Error (RMSE), the impact of each variable on the electricity consumption is analysed with this method. The most critical determinant is found to be the household size, while income level and heating type are discovered as 2nd and 3rd most prominent determinants for household electricity demand. With the help of the Partial Dependence Plots (PDP) provided by the GBRT method, the impact of each categorical and continuous variable is presented. Using the results of PDPs, the monetary values of both electricity generation and the social cost of CO2 emissions emitted into the atmosphere due to electricity generation are calculated for the most important determinants.

Original languageEnglish
Article number101312
JournalEnergy for Sustainable Development
Volume77
DOIs
Publication statusPublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 International Energy Initiative

Keywords

  • Gradient Boosting Regression tree
  • Household Budget Survey
  • Household electricity demand
  • Machine Learning
  • Social cost of Carbon
  • Turkey

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