论文标题
通过整体机器学习预测高峰日和高峰时段的电力需求
Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning
论文作者
论文摘要
电池储能系统可用于减少电力系统的高峰需求，从而带来巨大的经济利益。两个实际的挑战是1）准确确定峰值载荷天数和小时以及2）量化和减少与概率风险措施进行派遣决策的预测相关的不确定性。在这项研究中，我们开发了一种监督的机器学习方法来生成1）下一个运营日的概率包含本月的高峰时段，2）一个小时的概率是一天中的高峰时段。提供了有关数据的准备和增强以及机器学习模型和决策阈值的选择。所提出的方法应用于杜克能源进度系统，并成功捕获了72个测试月份的69个高峰日，超过3％的概率阈值。在高峰日的90％中，实际的高峰小时是最高概率的2小时。
Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on the preparation and augmentation of data as well as the selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 hours with the highest probabilities.
