论文标题
每日电力负载和曲线回归的分位数的概率预测
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
论文作者
论文摘要
对电力负载曲线的概率预测对于日益波动和竞争性的能源市场的有效调度和决策至关重要。我们提出了一种新的方法来构建曲线（PPC）的概率预测因子，该方法在曲线到曲线线性回归的背景下导致了对分位数的自然和新定义。 PPC有三种类型：预测集，一个预测频带和一个预测分位数，所有这些都在预先指定的名义概率水平上定义。在模拟研究中，PPC在各种数据生成机制下实现了有希望的覆盖概率。在预测法国每日电力载荷曲线的预测前一天，PPC在预测准确性，覆盖率和预测带的平均长度方面优于几种最先进的预测方法。预测分位数曲线提供了有见地的信息，这些信息与电力供应管理中的对冲风险高度相关。
Probabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC), which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predictive set, a predictive band and a predictive quantile, all of which are defined at a pre-specified nominal probability level. In the simulation study, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management.
