Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Micro-Moment Classification for Anomalous Power Consumption Detection using 1D CNN(Institute of Electrical and Electronics Engineers Inc., 2023) Nayak, R.; Jaidhar, C.D.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.Item Smart Appliance Abnormal Electrical Power Consumption Detection(Springer Science and Business Media Deutschland GmbH, 2024) Nayak, R.; Jaidhar, C.D.Potential cyber threats now have an immensely larger attack surface due to the widespread use of smart devices and smart environments. Smart home appliances build a network of linked objects that exchange information and communicate with each other. Detecting abnormal electrical power consumption becomes a first line of protection for bolstering the security of smart homes. Using Machine Learning (ML), anomalous electrical power consumption of the Smart Appliance can be identified. This work proposes an ML-based anomalous electrical power consumption detection to identify the security breach of the Smart Appliances. SimDataset is used for anomalous power consumption detection as a proof of concept for experimentation, and results depicted that Random Forest (RF) classifier outperformed other ML-based classifiers while detecting the abnormal electrical power usage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Experimental Study on Detection of Household Electrical Appliance Energy Consumption Deviation(Springer Science and Business Media Deutschland GmbH, 2024) Nayak, R.; Jaidhar, C.D.The energy efficiency of buildings is compromised due to the wastage of power and the unidentified abnormal power consumption. Identifying the patterns within a dataset that drastically vary from the usual pattern or behavior is known as anomaly detection. With anomalous power consumption detection, it is possible to respond quickly to problems like malfunctioning appliances, energy waste, or unusual usage patterns, improving energy management, reducing costs, and improving safety. This work is an experimental study on detecting electrical appliance energy consumption deviation using a micro-moment labeled appliance power consumption dataset named ‘SimDataset’. Two sets of experiments were conducted: the first was by using the original dataset without removing any features, and in the second experiment, highly correlated redundant features were removed from the original dataset. Experiments are conducted based on an 80:20 split of the dataset and also on tenfold cross-validation. Experimental results depicted that the Random Forest (RF) classifier performed best, and its performance is consistent among different experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.