考虑随机不确定性估计的可变客户组合能耗预测
Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation
摘要：
使用智能电表记录的每小时能耗数据，零售商可以估计其客户组合的前一天能耗。深度神经网络特别适合这项任务，因为智能电表记录中有大量的历史消费数据可用于模型训练。概率层还能够估计消费预测的不确定性。在这里，我们提出了一种方法来计算每小时的前一天的能源消耗预测，其中包括任意不确定性的估计。为了考虑能耗值的统计特性，使用对数正态分布对任意不确定性进行建模，其参数由深度神经网络计算。其结果是，预测的每小时一天前的能源消耗的单个客户表示的随机变量从对数正态分布获得的输出从神经网络。我们进一步证明，这些随机变量对应于单个客户可以聚集到任意组成的客户组合的概率预测。
英文摘要：
Using hourly energy consumption data recorded by smart meters, retailers can estimate the day-ahead energy consumption of their customer portfolio. Deep neural networks are especially suited for this task as a huge amount of historical consumption data is available from smart meter recordings to be used for model training. Probabilistic layers further enable the estimation of the uncertainty of the consumption forecasts. Here, we propose a method to calculate hourly day-ahead energy consumption forecasts which include an estimation of the aleatoric uncertainty. To consider the statistical properties of energy consumption values, the aleatoric uncertainty is modeled using lognormal distributions whose parameters are calculated by deep neural networks. As a result, predictions of the hourly day-ahead energy consumption of single customers are represented by random variables drawn from lognormal distributions obtained as output from the neural network. We further demonstrate, how these random variables corresponding to single customers can be aggregated to probabilistic forecasts of customer portfolios of arbitrary composition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
