Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts

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Abstract

Peak load demand forecasting is a key exercise undertaken to avoid system failure and power blackouts. In this paper, the next day's peak load demand is forecasted. The challenge is to estimate a model that is capable of preventing underprediction of the peak load demand: in other words, a model that is competent in forecasting the upper bound of the peak demand to avoid the risk of power blackouts. First, quantile regression is performed to generate forecasts of the daily peak load demand. Then, peak demand forecasts are locally approximated by triangular distribution to generate the upper bound of the peak demand. The forecasted upper bounds are compared with the actual electricity demand. The proposed method succeeds in avoiding underprediction of the peak load demand and thus the risk of power blackouts.

Keywords: Electricity peak demand, Quantile regression, Triangular distribution, Blackouts.

JEL Classifications: Q47, C21

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Author Biographies

Niematallah Elamin, University of Khartoum,

Assistant Professor, School of Management Studies

Mototsugu Fukushige, Osaka University

Professor, Graduate School of Economics

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Published

2018-09-05

How to Cite

Elamin, N., & Fukushige, M. (2018). Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts. International Journal of Energy Economics and Policy, 8(5), 119–124. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/6742

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