@article{7622156b954a4ed8b4af0408b7d9c364,
title = "Unintended biases of an electricity demand forecast based on a double-log regression",
abstract = "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.",
keywords = "Double-log regression, Electricity demand forecast, Forecast bias, Forecast precision",
author = "Woo, {Chi Keung} and J. Zarnikau and Cao, {Kang Hua}",
note = "Funding Information: C.K. Woo{\textquoteright}s research is funded by research grants ( #4388 and #4400 ) from the Education University of Hong Kong . J. Zarnikau{\textquoteright}s contribution to this paper is part his ongoing research on electricity economics at UT Austin. Funding Information: C.K. Woo's research is funded by research grants (#4388 and #4400) from the Education University of Hong Kong. J. Zarnikau's contribution to this paper is part his ongoing research on electricity economics at UT Austin.",
year = "2020",
month = dec,
doi = "10.1016/j.tej.2020.106866",
language = "English",
volume = "33",
journal = "Electricity Journal",
issn = "1040-6190",
publisher = "Elsevier Inc.",
number = "10",
}