Regression model-based electrical load anomaly detection shows great potential to improve
the quality of demand side management (DSM) because the load prediction and detection …

We propose a unified machine learning (ML) framework for simultaneously performing
electrical load forecasting and unsupervised anomaly detection in real time. For load …

Buildings account for a significant portion of the world's electricity consumption, including
industrial, commercial, residential, hospital buildings, etc. During building operation …

Anomaly detection in home power monitoring can be categorized into two main types:
detection of electrical theft, leakage, or nontechnical loss and monitoring anomalies in the …

Electricity theft has caused enormous damage to grid's safety and economy globally,
bringing plentiful attention to electricity theft detection. However, the inherent problems of …

The analysis of abnormalities in smart meter data has applications in load forecasting, cyber
security, fault detection, electricity theft detection, demand response, etc. Abnormality is …

The detection of abnormal electricity consumption behavior has been of great importance in
recent years. However, existing research often focuses on algorithm improvement and …

During building operation, a significant amount of energy is wasted due to equipment and
human-related faults. To reduce waste, today's smart buildings monitor energy usage with …

Although the recent load information is critical to very short-term load forecasting (VSTLF),
power companies often have difficulties in collecting the most recent load values accurately …

Identification of abnormal power consumption data is a critical and difficult task to guarantee
efficiency and reliability of the smart grid. This paper proposes a power consumption …