Author Description

Anomaly detection is an important topic which has been well‐studied in diverse research areas and application domains. It generally involves detection of abnormal data, unhealthy status, fault diagnosis, and can be helpful to guarantee industrial systems’ stability, security, and economy. As development of intelligent industries and sensor systems grows, large amounts of data become easily available, and challenges arise in industrial systems’ anomaly detection. One typical case is the study within energy‐related systems, like thermal energy, renewable energy study (e.g., wind energy, photovoltaic), electric vehicles, and so on. These systems can involve various data formats and more complex data structures making anomaly data detection a challenge. Currently, under the development of deep learning and big data analytics, many promising results have been achieved in energy systems’ anomaly data detection. However, many challenging problems remain unsolved due to the complex nature of energy industries. New techniques and advanced engineering applications on anomaly detection in energy systems still appeal to a wide range of scholars and industries.