Quantile Regression for Peak Demand Forecasting
Quantile Regression for Peak Demand Forecasting
复制标题
峰值需求预测的分位数回归
DOI：
10.2139/ssrn.2485657
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Faruqui
中科院分区：
文献类型：
--
作者：
C. Gibbons;A. Faruqui
我们证明了年度需求高峰日的特点是预测因子的极值(如天气)和对需求的巨大的不可预测的“冲击”。OLS方法结合了前一种特征，但不包括后者，导致OLS对年度峰值的估计向下倾斜。我们发展了一种新的估计方法--最优预测分位数回归(OFQR)，它使用分位数回归来估计日高峰需求模型，然后使用损失函数框架来估计分位数来预测年度峰值。我们比较了OLS和OFQR估计方法在32个公用设施区的结果。虽然OFQR方法是公正的，但OLS平均低估了近5%。此外，OFQR将平均绝对百分比误差降低了43%。自举过程生成具有准确95%的样本覆盖率和87%的样本外覆盖率的预测区间。
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.