Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

Maintenance decisions in domains such as aeronautics are becoming increasingly
dependent on being able to predict the failure of components and systems. When data …

In the field of predictive maintenance (PdM), machine learning (ML) has gained importance
over the last years. Accompanying this development, an increasing number of papers use …

Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the
recent years, Argumentation has been used for providing Explainability to AI. Argumentation …

Due to the frequently changed outdoor weather conditions and indoor requirements,
heating, ventilation and air conditioning (HVAC) experiences faulty operations inevitably …

In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

Financial markets are a real life multi-agent system that is well known to be hard to explain
and interpret. We consider a gradient boosting decision trees (GBDT) approach to predict …

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced
efficiency, automation, accuracy, cost savings, and independence in maintenance …

Although deep learning models have been rapidly developed, their practical applications
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …