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Significance
In the United States alone, buildings are responsible for 40% of the total energy consumption. Going with the current trends, future demands are expected to escalate thereby overstraining the presently inadequate energy sources. As such, reducing energy consumption in buildings is a crucial aspect for the development and sustainability of future modern cities/homes. The first step in minimizing energy use, is by first knowing how the energy is spent or utilized before retrofitting is undertaken. These data help in making retrofitting decisions. Presently, existing plausible literature has shown that collection of the required data involves the use of manual systems- which are tedious and time consuming, or the use of Building Automation Systems (BAS) and sensors- which have prohibitive installation costs and not suitable for antique buildings. Therefore, since it is apparent that a substantial amount of data needs to be continuously collected, managed, and analyzed in a building at the room and floor level in order to effectively optimize energy use in buildings, it is imperative that a smart data collection system that overcomes the enlisted shortcomings be developed.
To this note, University of Michigan scientists: Dr. Bharadwaj Mantha (currently post-doctoral fellow), Professor Carol Menassa and Professor Vineet Kamat from the Department of Civil and Environmental Engineering developed a framework that used autonomous mobile indoor robots for gathering actionable building information in real-time. They also showed how the collected information could be used for various analyses and critical decision-making. In particular, they developed a generic frame work for making informed retrofit decisions with the help of robot collected data. Their work is currently published in the research journal, Automation in Construction.
In brief, the researchers first built an autonomous indoor robot installed with RGB cameras and sparse sensors. Next, they developed navigation and drift correction algorithms for the autonomous robot operation. The navigation system was then tested in a large area. Lastly, a case study was performed so as 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.
The authors observed that the simulations undertaken demonstrated the feasibility of their approach along with its energy saving potential. Specifically, simulated annual energy savings of 3% were obtained by slightly modifying the R-values of one of the external wall assemblies. In addition, the analysis results illustrated how the proposed approach could be used to make informed retrofit decisions.
In summary, the study by University of Michigan researchers presented a novel data collection approach for ambient parameters, that utilize mobile autonomous robots mounted with sparse sensors that provide real-time data regarding parameters of interest. Generally, good energy saving levels were achieved for the wall used in the case study, and the saving could be larger if the whole building retrofit and upgrade of material was to be performed. Altogether, the proposed framework offers promise in improving the energy efficiency by extending the approach to other combinations of building materials. It also offers an efficient and economical technique for data collection that outperforms its predecessors.
Reference
Bharadwaj R.K. Mantha, Carol C. Menassa, Vineet R. Kamat. Robotic data collection and simulation for evaluation of building retrofit performance. Automation in Construction, volume 92 (2018) page 88–102.
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