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

Chi Keung WOO; J. ZARNIKAU; K.H. CAO

doi:10.1016/j.tej.2020.106866

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

Chi Keung WOO
, J. ZARNIKAU
, K.H. CAO

Department of Asian and Policy Studies (APS)

Research output: Contribution to journal › Articles › peer-review

3 Citations (Scopus)

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. Copyright © 2020 Elsevier Inc. All rights reserved.

Original language	English
Article number	106866
Journal	The Electricity Journal
Volume	33
Issue number	10
Early online date	10 Oct 2020
DOIs	https://doi.org/10.1016/j.tej.2020.106866
Publication status	Published - Dec 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

Double-log regression
Electricity demand forecast
Forecast bias
Forecast precision

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10.1016/j.tej.2020.106866

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

Abstract

UN SDGs

Keywords

Access to Document

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