Micro-Moment Classification for Anomalous Power Consumption Detection using 1D CNN

Abstract

Identifying anomalous power consumption is essential in improving energy efficiency in buildings. With the help of sensors and other intelligent systems installed in buildings (including smart homes), identifying anomalous power consumption becomes easy. In this work, 1 Dimensional Convolutional Neural Network (1D CNN)-based classification model is proposed to classify the micro-moments to identify the anomalous power consumption in the presence and absence of the consumer. The SimDataset values are normalized, and each instance with ten features is given as input to the 1D CNN. The robustness of the proposed model is defined by experimenting with varying the hyperparameter to obtain the best performance in the standard performance evaluation metrics. The results depicted that the suggested model outperformed the state-of-the-art, producing an accuracy of 96.4% and a weighted average F1-score of 0.962. © 2023 IEEE.