Technologies and Applications of Data-Driven Anomaly Detection in Energy Systems

Dear Colleagues,

The energy sector is becoming more and more information-intensive due to the growing spread and penetration of IOT sensing and long-term smart monitoring infrastructures. The knowledge hidden in massive operational data, collected from energy systems, can bring significant benefits for the characterization and modeling of their actual performance during operation. As a consequence, the robust coupling of data-driven technologies and energy domain knowledge proved to be effective in achieving relevant energy savings by exploiting a variety of advanced energy management solutions. In this context, anomaly detection systems play a key role in the new paradigm of data-driven energy systems management allowing the prompt and automatic recognition of abnormal and non-optimal performance patterns, providing information for the identification of energy waste and for the prioritization of corrective interventions. Such tools typically rely on (i) the automatic recognition of typical/normal energy behaviour of the system under investigation (ii) and on the consequent detection of anomalous/infrequent energy patterns, often leveraging predictive analytics, data mining and pattern recognition techniques. The development of a data-driven anomaly detection process poses several challenges related to the definition of anomaly itself. In the case of energy systems the definition of anomaly is very domain-specific and may pertain to faulty operations of system appliances/components, incorrect system management/control strategies, abnormal system energy consumption and technical/non-technical energy losses. Despite data-driven anomaly detection processes being able to lead to systematic energy saving during the operation of energy systems with a short simple payback period, their market penetration is still not satisfactory. At present, interesting research and technological opportunities are wide open also considering the current momentum of AI, and the increasing need of automatic management solutions for energy systems in relevant sectors such as industry, renewables and buildings. In this context, there is the need to deepen research and improve the effectiveness and reliability of anomaly detection processes. The objective of this Topic Project is to showcase the diversity and advances in this research field and to contribute in exploring the scalability of such data-driven solutions for a large set of heterogeneous energy systems. Original papers based on the analysis of both experimental and simulated energy system data are welcome. We are particularly interested in receiving manuscripts that integrate energy, mechanical and computer engineering research. We invite researchers from all areas of engineering to submit manuscripts for this relevant Topic Project.

Dr. Marco Savino Piscitelli
Dr. Alfonso Capozzoli
Prof. Dr. Antonio Rosato
Dr. Cheng Fan
Topic Editors