基于决策树的自动编码网络的异常能耗检测方法
文献来源
（1）Himeur, Yassine, et al. “A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks.” Cognitive Computation 12.6 (2020): 1381-1401.
（2）Himeur, Yassine, et al. “Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors.” International Journal of Intelligent Systems 36.6 (2021): 2865-2894.
（3）Aguilar D L, Perez M A M, Loyola-Gonzalez O, et al. Towards an interpretable autoencoder: A decision tree-based autoencoder and its application in anomaly detection[J]. IEEE Transactions on Dependable and Secure Computing, 2022.
科研背景
选择SVM方法时：
DRED数据集准确率和F1-分数达到97.41%
QUD数据集准确率和F1-分数分别达到67.9%和44.59%，均不足70%
选择DNN和KNN方法时：
效果较好，但数据集还有可考虑的属性，例如：温度，湿度，季节等。
方法：
结果
