Experimental Study on Detection of Household Electrical Appliance Energy Consumption Deviation

Abstract

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.