Robotic data collection and simulation for evaluation of building retrofit performance

Abstract Building engineers need access to real-time data to make retrofit decisions or improve the energy performance of the building occupants. Such data is typically gathered with the help of fixed wired and/or wireless sensors. Such a process consumes a significant amount of time, effort, and financial resources, particularly in existing buildings which do not have a Building Automation Systems (BAS) installed. This paper introduces a framework that uses autonomous mobile indoor robots for gathering actionable building information in real-time, and discusses how this information can be further utilized for various analyses and critical decision-making. The navigation and drift correction algorithms developed for autonomous robot operation are described in detail. Experimental results demonstrate the feasibility and applicability of the proposed method in large areas using only a sparse set of sensors mounted on mobile indoor robots. This research also developed a generic framework for making informed retrofit decisions with the help of robot collected data. A case study is performed to demonstrate the informed retrofit decision making process with the help of temperature data collected by a robot and subsequently used in an EnergyPlus simulation. Simulated annual energy savings of 3% were obtained by slightly modifying the R-values (20% to 45% improvement) of one of the external wall assemblies. These savings are expected to be larger if whole building retrofit and upgrade of material is performed. Thus, the proposed framework offers promise in improving the energy efficiency by extending the approach to other combinations of building materials (e.g. doors and windows).