The inclusion of conductor temperature variations for numerous power system planning and operational studies has long been recognized in the literature. The conductor temperature is majorly affected by environmental factors such as the ambient temperature. An efficient forecasting technique for forecasting ambient temperature is the need of the hour to prevent unexpected hazards in power systems and other areas caused due to temperature variations. Numerous researches have proposed different point forecasting and probabilistic forecasting models for the forecasting of ambient temperature. The probabilistic forecasting of ambient temperature provides complete information about future uncertainties, therefore quantifying the effects of daily temperature variations. A forecast combination approach, such as the quantile regression averaging, has never been utilized in this context. The selection of suitable point forecasters that complement each other's effects by characterizing different aspects of the ambient temperature data for averaging and the use of frequency components that explain the daily and periodic seasonal variations of ambient temperature to construct a forecasting model are the significant features of this paper. The proposed model is used with four varieties, and each is compared with the others. It is found that the variant using all the complementary individual point forecasters performs better in making probabilistic forecasts than the other options as well as better than the popular quantile k‐nearest neighbors, quantile egression forests, and basic quantile regression as inferred from the quantile score, Winkler score, and reliability plots.

中文翻译：

---

基于分位数回归平均的每日环境温度概率预测

早已在文献中认识到将导体温度变化包含在众多电力系统规划和运行研究中。导体温度主要受环境因素（例如环境温度）的影响。用于预测环境温度的有效预测技术是需要一小时的时间，以防止由于温度变化而在电力系统和其他区域产生意外危害。许多研究已经提出了用于环境温度预测的不同点预测和概率预测模型。环境温度的概率预测提供了有关未来不确定性的完整信息，因此可以量化每日温度变化的影响。预测组合方法，例如分位数回归平均，在这种情况下从未使用过。通过描述环境温度数据不同方面的平均值来选择相互补充效果的合适点预报器，并使用解释环境温度的每日和周期性季节性变化的频率分量来构建预报模型，这是其重要特征纸。所提出的模型用于四个品种，并将每个品种与其他品种进行比较。结果发现，使用所有互补的单个点预测器的变体在进行概率预测方面比其他选项更好，并且比从分位数得分推断出的流行分位数k最近邻，分位数外出森林和基本分位数回归更好，Winkler得分和可靠性图。