Department of Industrial Security, Chung-Ang University, Seoul, Republic of Korea.
School of Electrical Engineering, Korea University, Seoul, Republic of Korea.
Comput Intell Neurosci. 2022 Feb 8;2022:6892995. doi: 10.1155/2022/6892995. eCollection 2022.
Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based incremental learning model to perform accurate and interpretable DPLF and TDLF. To this end, we employ time-series cross-validation to effectively reflect recent electrical load trends and patterns when constructing the model. We also analyze variable importance to identify the most crucial factors in the Cubist model. In the experiments, we used two publicly available building datasets and three educational building cluster datasets. The results showed that the proposed model yielded averages of 7.77 and 10.06 in mean absolute percentage error and coefficient of variation of the root mean square error, respectively. We also confirmed that temperature and holiday information are significant external factors, and electrical loads one day and one week ago are significant internal factors.
从一天到一周后的最优电力系统运行需要进行每日高峰负荷预测 (DPLF) 和总日负荷预测 (TDLF)。本研究开发了基于 Cubist 的增量学习模型，以进行准确和可解释的 DPLF 和 TDLF。为此，我们采用时间序列交叉验证来构建模型时有效地反映最近的电力负荷趋势和模式。我们还分析了变量的重要性，以确定 Cubist 模型中最关键的因素。在实验中，我们使用了两个公开可用的建筑物数据集和三个教育建筑物集群数据集。结果表明，所提出的模型的平均均方根误差的平均绝对百分比误差和变异系数分别为 7.77 和 10.06。我们还证实，温度和假期信息是重要的外部因素，而一天和一周前的电力负荷是重要的内部因素。
