Satish Narayanan

Energy efficient building environment control strategies using real-time occupancy measurements

Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may allow significant energy-savings by intelligent control of the L-HVAC systems. In this paper, we report on the deployment of a wireless camera sensor network for collecting data regarding occupancy in a large multi-function building. The system estimates occupancy with an accuracy of 80%. Using data collected from this system, we construct multivariate Gaussian and agent based models for predicting user mobility patterns in buildings. Using these models, we can predict room usage thereby enabling us to control the HVAC systems in an adaptive manner. Our simulations indicate a 14% reduction in HVAC energy usage by having an optimal control strategy based on occupancy estimates and usage patterns.

A sensor-utility-network method for estimation of occupancy in buildings

We introduce the sensor-utility-network (SUN) method for occupancy estimation in buildings. Based on inputs from a variety of sensor measurements, along with historical data regarding building utilization, the SUN estimator produces occupancy estimates through the solution of a receding-horizon convex optimization problem. State-of-the-art on-line occupancy algorithms rely on indirect measurements, such as CO2 levels, or people counting sensors which are subject to significant errors and cost. The newly proposed method was evaluated via experiments in an office building environment. Estimation accuracy is shown to improve significantly when all available data is incorporated in the estimator. In particular, it is found that the average estimation error at the building level is reduced from 70% to 11% using the SUN estimator, when compared to the naive approach that relies solely on flow measurements.

Model-Based Real-Time Estimation of Building Occupancy During Emergency Egress

This paper provides a viable and practical solution to the challenge of real-time estimation of the number of people in areas of a building, during an emergency egress situation. Such estimates would be extremely valuable to first responders to aid in egress management, search-and-rescue, and other emergency response tactics. The approach of this paper uses an extended Kalman filter, which combines sensor readings and a dynamic stochastic model of people movement. The approach is demonstrated using two types of sensors: video with real-time signal processing to detect number of people moving in each direction across a threshold such as an entrance/exit, and passive infra-red motion sensors that detect people occupancy within its field of view. The people movement model uses the key idea that each room has a “high-density” and “low-density” area, where high-density corresponds to a queue of people at a bottleneck exit doorway, and low-density represents unconstrained flow of people. Another key feature of the approach is that constraints on occupancy levels and people flow rates are used to improve the estimation accuracy. The approach is tested using a stochastic discrete-time simulation model of a 1500 square meter office building with occupancy up to 100 people, having a video camera at each of the three exits, and motion sensors in each of the 42 office rooms. The simulation includes stochastic models of video sensors having a probability of detection of 98%, and motion sensors with probability of detection of 80%. Averaged over 100 simulation runs and averaged over the evacuation time, the sensor-only approach produced a mean estimation error per room of 0.35 people, the Kalman filter with cameras only had a mean error of 0.14 people, and the Kalman filter with all sensors produced a mean error of 0.09 people. These results show that an effective combination of models and sensors greatly improves estimation accuracy compared to the state-of-the-art practice of using sensors only.

Parameter estimation of a building system model and impact of estimation error on closed-loop performance

Predictive-control methods have been recently employed for demand-response control of building and district-level HVAC systems. Such approaches rely on models and parameter estimates to meet comfort constraints and to achieve the theoretical system-efficiency gains. In this paper we present a methodology that establishes achievable targets for control-model parameter estimation errors based on closed-loop performance sensitivity. The control algorithm is designed as a Model Predictive Controller (MPC) that uses perturbed building-model parameters. We perform simulations to estimate the dependency of energy cost and constraint infringement time on the magnitude of these perturbations. The simulation results are used to define targets for the parameter estimation errors, which in turn are applied to specify the character of excitation and model structure used for identification. We design a parameter estimator and perform Monte-Carlo simulations for a model that includes sensor noise and load uncertainty. The distribution of the estimation errors are used to demonstrate that the established targets are met.

Multiscale consensus for decentralized estimation and its application to building systems

Multiscale approaches to accelerate the convergence of decentralized consensus problems are introduced. Consecutive consensus iterations are executed on several scales to achieve fast convergence for networks with poor connectivity. As an example the proposed algorithm is applied to the decentralized Kalman filtering problem for estimation of contaminants in building systems. Two conventional observers are designed and convergence is compared with respect to the number of communications necessary, which is an effective measure of system complexity. It is demonstrated that the proposed multiscale scheme substantially accelerates the decentralized consensus. Future extentions and directions are briefly summarized.

Anomaly detection using projective Markov models in a distributed sensor network

The paper develops application of techniques from robust and universal hypothesis testing for anomaly detection and change-point detection in dynamic, interconnected systems. This theory is extended using the concept of projected Markov models originally proposed by Claude Shannon. Also presented is a detailed application to anomaly detection from people movements patterns in buildings.

Reduced-order Dynamical Modeling of Sound Generation From a Jet

The flow and sound associated with the near field of a three-dimensional, unsteady jet flow are modeled using a moderate-dimensional vortex model, namely using inviscid, incompressible vortex filament simulations. The model captures the dynamics and sound generation from organized motion in the turbulent jet flow field, neglecting the fine-scale structure, viscous and compressibility effects. The jet flow model simulations are used to reproduce the mean and unsteady characteristics of the jet flow measured experimentally in a turbulent, Mj=0.6, cold, single stream jet. A low Mach number acoustic analogy is used to compute the noise source distribution and radiation associated with the jet. Sound generation associated with the organized flow structures and their interactions in the jet is investigated. Good comparisons of the spatial distribution of jet noise sources with experimental measurements are obtained, suggesting promise for the use of such dynamical models in the analysis and control of jet noise.

Reduced order modeling for contaminant transport and mixing in building systems: A case study using dynamical systems techniques

In this paper we propose a Lagrangian coherent structures (LCS) based approach to modeling and estimation of contaminant transport and mixing in large indoor spaces in buildings. Specifically, we show how the knowledge of LCS can be exploited to enhance proper orthogonal decomposition (POD) based model reduction, sensor placement and comparing effect of different control schemes. We illustrate this approach in a three-dimensional room equipped with a mechanical ventilation system.