A randomized forecasting method based on the generation of ensembles of entropy-optimal forecasting trajectories is developed. The latter are generated by randomized dynamic regression models containing random parameters, measurement noises, and a random input. The probability density functions of random parameters and measurement noises are estimated using real data within the randomized machine learning procedure. The ensembles of forecasting trajectories are generated by the sampling of the entropy-optimal probability distributions. This procedure is used for the randomized prediction of the daily electrical load of a regional power system. A stochastic oscillatory dynamic regression model is designed. One-, two-, and three-day forecasts of the electrical load are constructed, and their errors are analyzed.

提出了一种基于熵最优预测轨迹集合生成的随机预测方法。后者由包含随机参数，测量噪声和随机输入的随机动态回归模型生成。随机参数和测量噪声的概率密度函数是使用随机机器学习过程中的实际数据估算的。预测轨迹的集合是通过对熵最佳概率分布进行采样而生成的。此过程用于区域电力系统的每日电力负荷的随机预测。设计了随机振荡动态回归模型。构造了电力负荷的一日，两日和三日预测，并分析了它们的误差。