Adaptive quantile estimation in performance monitoring of building automation systems

Heating, ventilation and air conditioning (HVAC) systems in buildings are subject to increasing expectations on performance. As such, HVAC systems need to be continuously monitored in order to early detect any anomalies which would degrade their performance. The building automation domain is subject to constraints which translate into the need for computationally efficient self-learning techniques. This paper provides an industrial R&D perspective on the topic with a more detailed focus on a modified algorithm for adaptive quantile estimation, which can be applied in various analytical workflows. This is shown on the examples of control performance monitoring and equipment fault detection. Real world results for control performance monitoring are presented and analyzed in more detail.