Accurately predicting the power load in certain areas is of great importance for grid management and power dispatching. A great deal of research has been conducted within the smart grid system community in developing an assortment of different algorithms that seek to increase the accuracy of these predictions. However, these predictions suffer from various sources of error, such as the variations in weather conditions, calendar effects, economic indicators, and many other sources, which are caused by the inherent stochastic and nonlinear characteristics of power demand. In order to quantify the uncertainty in load forecasting effectively, this paper proposes a comprehensive probability density forecasting method employing Gaussian process quantile regression (GPQR). GPQR is a type of Bayesian non-parametric method which can handle the uncertainties in power load data in a principled manner. Consequently, the probabilistic distribution of power load data can be statistically formulated. The effectiveness of the proposed method for short-term load forecasting has been assessed adopting the real dataset provided by American PJM electric power company. Numerical results demonstrate that the uncertainties in power load data can be effectively acquired based on the proposed method. Meanwhile, the competitive predictive performance could be yielded with respect to the conventional adopted methods.

准确预测某些地区的电力负荷对于电网管理和电力调度至关重要。在智能电网系统社区中，已经进行了大量研究，以开发各种不同的算法来寻求提高这些预测的准确性。但是，这些预测会受到各种误差源的影响，例如天气状况，日历影响，经济指标和许多其他源的变化，这些变化是由电力需求的固有随机性和非线性特征引起的。为了有效地量化负荷预测中的不确定性，本文提出了一种利用高斯过程分位数回归（GPQR）的综合概率密度预测方法。GPQR是一种贝叶斯非参数方法，可以以有原则的方式处理电力负荷数据中的不确定性。因此，可以统计地表示电力负荷数据的概率分布。采用美国PJM电力公司提供的真实数据集，评估了所提出的短期负荷预测方法的有效性。数值结果表明，该方法可以有效地获取电力负荷数据的不确定性。同时，相对于常规采用的方法，可以产生竞争性的预测性能。采用美国PJM电力公司提供的真实数据集，评估了所提出的短期负荷预测方法的有效性。数值结果表明，该方法可以有效地获取电力负荷数据的不确定性。同时，相对于常规采用的方法，可以产生竞争性的预测性能。采用美国PJM电力公司提供的真实数据集，评估了所提出的短期负荷预测方法的有效性。数值结果表明，该方法可以有效地获取电力负荷数据的不确定性。同时，相对于常规采用的方法，可以产生竞争性的预测性能。