Quantile Regression for Peak Demand Forecasting

25 Pages Posted: 24 Aug 2014

Date Written: July 31, 2014


We demonstrate that annual peak demand days are characterized by both extreme values of predictors (such as weather) and large unpredictable "shocks" to demand. OLS approaches incorporate the former feature, but not the latter, leading OLS to produce downwardly-biased estimates of the annual peak. We develop a new estimation procedure, optimal forecast quantile regression (OFQR), that uses quantile regression to estimate a model of daily peak demand, then uses a loss function framework to estimate a quantile to predict the annual peak. We compare the results of the OLS and OFQR estimation approaches for 32 utility zones. While the OFQR approach is unbiased, OLS under-forecasts by nearly 5% on average. Further, OFQR reduces the average absolute percent error by 43%. A bootstrapping procedure generates forecast intervals with accurate 95% coverage in sample and 87% coverage out of sample.

JEL Classification: C53, C13, C15

Suggested Citation

Gibbons, Charles and Faruqui, Ahmad, Quantile Regression for Peak Demand Forecasting (July 31, 2014). Available at SSRN: https://ssrn.com/abstract=2485657 or http://dx.doi.org/10.2139/ssrn.2485657

Charles Gibbons (Contact Author)

The Brattle Group ( email )

201 Mission Street Suite 2800
San Francisco, CA 94105
United States
(415) 217-1055 (Phone)

HOME PAGE: http://gibbons.bio

Ahmad Faruqui

The Brattle Group ( email )

Suite 2800
201 Mission Street
San Francisco, CA 94105
United States

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