Faculty Publications
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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 Data-driven models for electricity theft and anomalous power consumption detection: a systematic review(Springer, 2025) Nayak, R.; Jaidhar, C.D.To maintain the effectiveness, dependability, and security of modern energy systems, analyzing and detecting anomalies in energy usage, such as electricity theft and unusual power consumption, is crucial as Smart Grid (SG) technologies become increasingly common. This survey paper comprehensively reviews the literature on energy consumption analysis and detection, focusing on detecting electricity theft and anomalous power consumption. The works that are considered in this paper are classified based on Machine Learning (ML), Deep Learning (DL), and hybrid models, to identify electricity theft and unusual power usage. Privacy preservation-based methodologies in the context of energy consumption research and summarize the survey articles. Furthermore, datasets used in electricity theft and anomalous power consumption detection, applications, challenges, and limitations related to detecting abnormal power usage and electricity theft are also discussed, and suggested future research paths to push the boundaries of this field of work. This survey study offers a thorough overview of current research trends and directions in energy consumption analysis and detection by synthesizing ideas from various studies. It benefits researchers, practitioners, and policymakers in the energy sector. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.