Abstract
Anomaly detection is a significant application of residential appliances load monitoring systems. As an essential prerequisite of load diagnosis services, anomaly detection is critical to energy saving and occupant comfort actualization. Notwithstanding, the investigation into diagnosis of household anomalous appliances has not been decently taken into consideration. This paper presents an extensive study about operation-time anomaly detection of household devices particularly, refrigerators, in terms of appliances candidate, by utilizing their energy consumption data. Energy as a quantitative property of electrical loads, is a reliable information for a robust diagnosis. Additionally, it is very practical since it is low-priced to measure and definite to interpret. Subsequently, an on-line anomaly detection approach is proposed to effectively determine the anomalous operation of the household appliances candidate. The proposed approach is capable of continuously monitoring energy consumption and providing dynamic information for anomaly detection algorithms. A machine learning-based technique is employed to construct efficient models of appliances normal behavior with application to operation-time anomaly detection. The performance of the suggested approach is evaluated through a set of diagnostic tests, by utilizing normal and anomalous data of targeted devices, measured by an acquisition system. In addition, a comparison analysis is provided in order to further examine the effectiveness of the developed mechanism by exploiting a public database. Moreover, this study elaborates sensible remarks on an effective management of anomaly detection and diagnosis decision phases, pivotal to correctly recognition of a faulty/abnormal operation. Indeed, through experimental results of case studies, this work assists in the development of a load monitoring and anomaly detection system with practical implementation.
Highlights
With a 66.5 TWh electricity saving potential, residential sector becomes the world primary energy saving target among end-use sectors
This paper provides a comprehensive study on household appliance-level anomaly detection by using energy consumption information of a smart and a standard refrigerator as appliances candidate
Our proposed mechanism for anomaly detection is the consequence of an exhaustive investigation into the behavior of the case studies based on their energy consumption
Summary
With a 66.5 TWh electricity saving potential, residential sector becomes the world primary energy saving target among end-use sectors. These actions are individually normal, their occurrence in a wrong sequence can lead to a collective anomaly From another viewpoint, anomaly detection methods are classified into ‘data-driven’ and ‘model-based’ practices, according to the way of acquiring a priori knowledge. From the standpoint of formulating an anomaly detection problem, machine-learning techniques have been widely utilized [17], [18] In this regard, three different mechanisms can be defined, accounting for: ‘Supervised’, that is training a classifier by using labeled classes of both normal and anomalous data instances; ‘Semisupervised’, that is training only by utilizing a labeled set of normal data; ‘Unsupervised’, that requires no training set since it groups the data under several clusters and defines dissimilar samples as anomaly. The aforementioned perspectives can be further explored in the specified references
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