Energy Consumption Profiling and Anomaly Detection in Smart Homes Using ...
The integration of smart technologies in residential environments has revolutionized energy management by enabling real-time monitoring and control of consumption patterns. This study presents a ...
The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home . We employ three outlier-based techniques for anomaly detection : (1) local outlier factor, (2) connectivity-based outlier factor, and (3) cluster-based local outlier factor.
A Lightweight Real
Thus, the paper has designed and implemented a lightweight system for real-time analyzing energy consumption and detecting anomalies in smart homes . The component of anomaly detection is built using statistical methods by comparing the energy consumption of one house with that of all other houses, allowing for quick and effective anomaly detection .
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly . Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it ...
PDF Anomaly Detection in Smart Home Electrical Appliances Using Machine ...
This study proposes an optimization framework for energy consumption anomaly detection for smart home IoT sensors and devices. The suggested model uses the strong LSTM algorithm for time series forecasting and two statistical models, ARIMA and SARIMAX, to create the best model.
Anomaly detection can provide feedback on energy consumption for problem diagnosis, which benefits energy suppliers and ecosystems [23, 110, 111, 112]. Electrical load anomaly detection methods can be categorized into two main groups: regression model-based and classification model-based [22].
PDF Anomaly Detection in Residential Electricity Consumption with GAN
Abstract—Efficient management of energy resources relies heavily on residential electricity usage. However, challenges such as defective smart meters, erratic consumption patterns, and energy theft pose serious threats to the stability of the smart grid. Addressing these irregularities requires robust anomaly detection methods.
This work introduced a novel data analytics-based methodology for anomaly detection in building energy consumption time series, explicitly separating the prediction of daily load profile shapes from the estimation of daily energy magnitudes.
Deep Learning
In this study, we evaluate the performance of four different encoder-decoder models for anomaly detection in building smart meter data using a reconstruction-based approach. The results are compared against an ARIMA-based model, which serves as the baseline. Model training and evaluation are carried out using Nested Cross-Validation for time series, a robust model assessment technique that ...
Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption . However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically ...