Load forecasting is an expected ability of electric power networks to enable effective capacity planning. This paper proposes a probabilistic approach to short-term load forecasting (STLF) of residential power consumption. The proposed method is based on Bayesian regression modeling. It utilizes an additive Gaussian Process (GP) to estimate climate-sensitive and calendar factors of power demand. The GP model is constructed by using a set of compositional kernels that represent the most significant interactions between input variables. Such collection is built up through a sampling method, capable of selecting the n-upmost order-based interactions. Moreover, a technique is performed to deal with challenges related to multivariate input and large dataset training complexity. The forecasting model is applied to actual power consumption data of a set of houses, located in Quebec, during winter. The results demonstrate that the suggested scheme is highly efficient to model and predict residential electricity use. Furthermore, it is competitive with other forecasting algorithms, as manifested by a comparative analysis.

负荷预测是电力网络实现有效容量规划的预期能力。本文提出了一种住宅用电短期负荷预测 (STLF) 的概率方法。所提出的方法基于贝叶斯回归建模。它利用加性高斯过程 (GP) 来估计电力需求的气候敏感和日历因素。GP 模型是通过使用一组表示输入变量之间最重要交互作用的组合核来构建的。这种集合是通过采样方法建立的，能够选择基于 n 个最高顺序的交互。此外，还执行了一种技术来应对与多变量输入和大型数据集训练复杂性相关的挑战。该预测模型应用于位于魁北克的一组房屋在冬季的实际用电量数据。结果表明，所建议的方案对于模拟和预测住宅电力使用非常有效。此外，正如比较分析所表明的那样，它与其他预测算法具有竞争力。