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.
The objective of this Research Topic is to solicit papers on recent developments in anomaly detection techniques and advances in applications of energy-related systems. The topic can cover techniques related to anomaly detection algorithm development, such as machine learning, data mining, deep learning, graph theory, big data etc.
Various aspects of energy applications can be addressed, like data cleaning, unhealthy evaluation of energy systems, condition monitoring and faults diagnosis in energy-related industries. Special attention could be paid to energy related systems such as wind energy, photovoltaic, thermal energy, electric vehicle (EV) development and so on.
Topics of interest include, but are not limited to:
• Theory development on anomaly detection
- Machine learning
- Deep learning
- Data Mining
- Big data analytics
- Graph theory
• Developed applications related to anomaly detection
- Data cleaning
- Abnormal data detection
- Anomaly detection
- Condition monitoring
- Fault diagnosis
• Anomaly detection in energy-related industrial systems
- Renewable energy research
- Power system research
- Wind and photovoltaic study
- Thermal systems
- Electric Vehicle (EV) systems.
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.
The objective of this Research Topic is to solicit papers on recent developments in anomaly detection techniques and advances in applications of energy-related systems. The topic can cover techniques related to anomaly detection algorithm development, such as machine learning, data mining, deep learning, graph theory, big data etc.
Various aspects of energy applications can be addressed, like data cleaning, unhealthy evaluation of energy systems, condition monitoring and faults diagnosis in energy-related industries. Special attention could be paid to energy related systems such as wind energy, photovoltaic, thermal energy, electric vehicle (EV) development and so on.
Topics of interest include, but are not limited to:
• Theory development on anomaly detection
- Machine learning
- Deep learning
- Data Mining
- Big data analytics
- Graph theory
• Developed applications related to anomaly detection
- Data cleaning
- Abnormal data detection
- Anomaly detection
- Condition monitoring
- Fault diagnosis
• Anomaly detection in energy-related industrial systems
- Renewable energy research
- Power system research
- Wind and photovoltaic study
- Thermal systems
- Electric Vehicle (EV) systems.