Optimal sensor placement strategy for office buildings using clustering algorithms

Abstract

Sensor networks embedded in the built environment provide critical information for intelligent building energy management. Data from these sensors enable optimizing energy efficiency and indoor environmental quality without compromising occupant comfort. Thus sensors help achieve efficient operation of building systems at reduced operating costs. Ideally, towards these goals all possible measurement points in buildings should be measured and verified. However, this would inevitably incur tremendous cost and time. Alternatively, an approach to identify the optimal measurement points that can provide a holistic picture of the indoor environment is desirable. This paper proposes a novel data driven approach based on field measurements in an office building to derive the optimal (number and locations of) measuring points. Clustering algorithms, information loss approach and Pareto principle were used to derive the optimal sensor placement strategy. The findings of this study can have important implications for researchers and practitioners.