Detection of Anomalies in Daily Activities Using Data from Smart Meters

Abstract

The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy consumption has also been dedicated to the areas of social work and health. In this context, smart meters are considered single-point non-intrusive sensors that might be used to monitor the behaviour and activity patterns of people living in a household. This work describes the design of a short-term behavioural alarm generator based on the processing of energy consumption data coming from a commercial smart meter. The device captured data from a household for a period of six months, thus providing the consumption disaggregated per appliance at an interval of one hour. These data were used to train different intelligent systems, capable of estimating the predicted consumption for the next one-hour interval. Four different approaches have been considered and compared when designing the prediction system: a recurrent neural network, a convolutional neural network, a random forest, and a decision tree. By statistically analysing these predictions and the actual final energy consumption measurements, anomalies can be detected in the undertaking of three different daily activities: sleeping, breakfast, and lunch. The recurrent neural network achieves an F1-score of 0.8 in the detection of these anomalies for the household under analysis, outperforming other approaches. The proposal might be applied to the generation of a short-term alarm, which can be involved in future deployments and developments in the field of ambient assisted living.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

“…Year Place Method Used for NILM Dataset Avg. Efficiency (%) [15] China Particle swarm 1 year 94.2 [16] Indonesia Random Forests 1 year 99 [17] India Markov Chain 31 days 94 [5] Estonia Extreme Gradient Boost (XgBoost) 3 years 97.2 [18] Malaysia K-NN, SVM, Ensemble 30 days 98.8 [19] Iran SVM 1 week 98.2 [20] Indonesia Convolutional Neural Networks (CNN) 1 month 98 [21] Italy Random Forests 27 months 96.3 [22] Spain Long Short-Term Memory Networks (LSTM) 7 months 98 [23] Greece Recurrent Neural network (RNN) 10 days 97 [24] Canada LSTM 2 days 98 [ The NILM method has been used for anomaly detection at the appliance level by incorporating machine learning [26]. In another study [8], NILM is utilized for the event matching of devices.…”

“…Evaluating NILM systems requires careful consideration since a single metric cannot capture all its nuances. Although metrics like mean squared error (MSE) and false positive/negative rates offer insights into overall accuracy, the evaluation should extend to specific appliance identification metrics such as precision, recall, and F1-score [22]. These metrics provide a granular understanding of how well the system distinguishes between individual appliances, which is essential for practical implementation in real-world scenarios [10,43].…”