Unintended biases of an electricity demand forecast based on a double-log regression

Chi-Keung WOO, J. Zarnikau*, Kang Hua CAO

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper identifies the unintended biases occasionally not recognized when using a double-log regression to make an electricity demand forecast. It shows that ignoring the stochastic nature of forecasts of income, price and weather can vastly overstate the forecast's precision, potentially causing inadequate resource procurement for reliable service at least cost. Fortunately, the overstated precision is readily avoidable because its correction uses information available when making the forecast.

Original languageEnglish
Article number106866
JournalElectricity Journal
Issue number10
Publication statusPublished - Dec 2020

Scopus Subject Areas

  • Business and International Management
  • Energy (miscellaneous)
  • Management of Technology and Innovation

User-Defined Keywords

  • Double-log regression
  • Electricity demand forecast
  • Forecast bias
  • Forecast precision


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