Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
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.Item Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.It is crucial to detect anomalous use of electrical power in critical systems to prevent malfunctions or hazards, ensure operational security, and optimize the energy economy. Since anomalies in critical systems can serve as early warning systems for potential issues or threats that could lead to severe failures, it becomes strategically crucial to discover them as soon as possible. This study proposes and suggests a novel technique for anomaly identification in industrial critical systems using a household appliance's electrical power consumption dataset in the absence of a dedicated critical system or industrial equipment dataset. The study looks at the ability of a deep learning (DL) model trained on household data to identify anomalous patterns in large-scale industrial equipment's power use. Convolutional neural network (CNN) is used in this work to analyze anomalous electrical power use based on micro-moments. In this work, an appliance-level dataset is employed for experimentation. 10 × 10 appliance-wise grayscale images are generated from numeric dataset with and without the instance-wise N-gram approach. The effectiveness of the proposed approach is evaluated and compared it with other ML and DL models used earlier. The experimental findings showed that the proposed approach worked better than other models. Compared to images created without the instance-wise N-gram approach, the performance of the proposed approach with images created with N-gram is superior. © 2001-2012 IEEE.Item Anomalous Electrical Power Consumption Detection in Household Appliances via Micro-Moment Classification(Institute of Electrical and Electronics Engineers Inc., 2025) Nayak, R.; Jaidhar, C.D.The detection of anomalous power consumption is critical for improving energy efficiency, particularly with the increasing demand in buildings. This study explores Convolutional Neural Network-based models by transforming 1-dimensional micro-moment labeled data into 2-dimensional matrices to capture both temporal and spatial consumption patterns. Three architectural variants are investigated: a conventional Deep Convolutional Neural Network (DCNN), a Depthwise Separable Convolutional Neural Network (DS-CNN), and a Depthwise Separable Residual Convolutional Neural Network (DSR-CNN). Unlike earlier studies, this work incorporates hyperparameter tuning, statistical validation, and cross-validation, resulting in the evaluation of over 450 model configurations. The results indicate that while the DCNN consistently achieves the highest accuracy, the DS-CNN achieves comparable performance with significantly reduced parameters and computational cost, making it suitable for real-time and resource-constrained environments. Model complexity analysis and statistical tests confirm the robustness of the findings. Finally, a systematic model selection strategy is presented, identifying the DS-CNN as the most balanced solution for effective and efficient anomaly detection in smart grid applications. © 2020 IEEE.