Equation-based modeling and optimization-based parameter estimation in multimodal virtual sensing platforms for smart buildings

Abstract

This paper considers a unified approach for virtual sensing in smart buildings that utilizes equation-based modeling and optimization-based parameter estimation. A modeling method is introduced to describe relationships among existing sensory information and application-oriented parameters in a reusable manner. Measurement errors in existing sensors are also described in this equation-based model. When parameters to be estimated and existing sensors used for virtual sensing are identified, relevant equations are selected and combined as simultaneous equations. The parameter estimation method takes an optimization-based approach to cope with uncertainty in sensory information. Simultaneous equations corresponding to target sensing are used as a constraint in the optimization problem. In cases where many different sensors are utilized to increase accuracy, we may need to consider compromising contradictions among sensory information due to sensing errors. If the target building does not have enough sensors for the intended parameter estimation, we may need certain assumptions to determine the value. Such over- or under-defined situations are automatically detected and considered in our parameter estimation mechanism. For this purpose, we introduce a method, based on an idea inspired by randomized algorithms, for repeatedly solving an optimization problem with different weight factors in the objective function and for analyzing any fluctuations in estimated values. This is computationally inexpensive and can be applied to big real-world problems. We applied this method to building occupancy estimations for efficient air-conditioning control and to customer attribute analysis for an office building cafeteria during lunch time.