A new data-driven controllability measure with application in intelligent buildings

Abstract

Buildings account for ca. 40% of the total energy consumption and ca. 20% of the total CO2 emissions. More effective and advanced control integrated into Building Management Systems (BMS) represents an opportunity to improve energy efficiency. The ease of availability of sensors technology and instrumentation within today's intelligent buildings enable collecting high quality data which could be used directly in data-based analysis and control methods. The area of data-based systems analysis and control is concentrating on developing analysis and control methods that rely on data collected from meters and sensors, and information obtained by data processing. This differs from the traditional model-based approaches that are based on mathematical models of systems. We propose and describe a data-driven controllability measure for discrete-time linear systems. The concept is developed within a data-based system analysis and control framework. Therefore, only measured data is used to obtain the proposed controllability measure. The proposed controllability measure not only shows if the system is controllable or not, but also reveals the level of controllability, which is the information its previous counterparts failed to provide. We use two illustrative examples to demonstrate the method, which also include an intelligent building.