Siddharth Goyal

Zone-level control algorithms based on occupancy information for energy efficient buildings

We examine the problem of how to use occupancy information of various fidelity to reduce the energy consumed in maintaining desired levels of thermal comfort and indoor air quality (IAQ) in commercial buildings. We focus on the zone-level control, where the control inputs to be decided are the supply air (SA) flow rate and the amount of reheat. We propose three control algorithms with varying information requirements: (i) POBOC, that requires long-horizon accurate prediction of occupancy and a model of the hygrothermal dynamics of the zone, (ii) OMBOC, that requires only occupancy measurement and a dynamic model, and (iii) Z-DCV, that requires only occupancy measurement. The first two strategies use a model predictive control framework to compute the optimal control inputs, while the third one is a pure feedback-based control strategy. Simulations with a calibrated model show that significant energy savings over a baseline controller, the kind usually used in existing buildings, is possible with the last two strategies, that is, even without occupancy prediction. Trade-offs between complexity and performance of the control algorithms are discussed.

A Method for model-reduction of nonlinear building thermal dynamics

We propose a method for model-reduction of a class of non-linear models that are relevant to modeling thermal dynamics of multi-zone buildings. These models can have large state-space dimension even for a moderate number of zones. Reduced order models of building thermal dynamics can be useful to model-based control for improving energy efficiency, especially to computationally intensive ones such as Model Predictive Control (MPC). Although there are a number of well-developed techniques for model reduction of LTI systems, the same cannot be said about non-linear systems. The method we propose exploits the linear portion of the model to compute a transformation (by using balanced realization) and a specific sparsity pattern of the non-linear portion to obtain the reduced order model. Simulations are presented with a four zone building model, which show that the prediction of the zone temperatures and humidity ratios by the reduced model is quite close to that from the full-scale model, even when substantial reduction of model order is specified.

Identification of multi-zone building thermal interaction model from data

Constructing a model of thermal dynamics of a multi-zone building requires modeling heat conduction through walls as well as convection due to air-flows among the zones. Reduced order models of conduction in terms of RC-networks are well established, while currently the only way to model convection is through CFD (Computational Fluid Dynamics). This limits convection models to a single zone or a small number of zones in a building. In this paper we present a novel method of identifying a reduced order thermal model of a multi-zone building from measured space temperature data. The method consists of first identifying the underlying network structure, in particular, the paths of convective interaction among zones, which corresponds to edges of a building graph. Convective interaction among a pair of zones is modeled as a RC network, in a manner analogous to conduction models. The second step of the proposed method involves estimating the parameters of the RC network model for the convection edges. The identified convection edges, along with the associated R and C values, are used to augment a thermal dynamics model of a building that is originally constructed to model only conduction. Predictions by the augmented model and the conduction-only model are compared with space temperatures measured in a multi-zone building in the University of Florida campus. The identified model is seen to predict the temperatures more accurately than a conduction-only model.

Effect of various uncertainties on the performance of occupancy-based optimal control of HVAC zones

Model Predictive Control (MPC) has emerged as a potential control architecture for operating buildings in a more energy efficient manner. We study through simulations the effect of several sources of uncertainty that arise in the implementation of MPC on the energy consumption, thermal comfort, and indoor air quality (IAQ). These include occupancy profile, measurement errors and mismatch between the plant and its model that the control algorithm uses. Simulations are carried out for two extreme cases: a winter day with no solar load and a summer day with high solar load. The study shows that increasing fluctuations in occupancy, errors in measuring occupancy, and model mismatch have the strongest impact on the energy consumption. However, measurement errors in outside temperature and solar load does not have significant impact. Therefore, it is possible to improve the controller performance by using more accurate occupancy sensors. Furthermore, implementation cost can also be reduced by eliminating the sensors and prediction algorithms for predicting outside temperature and thermal loads without compromising the controller performance. Even with these uncertainties, MPC delivers 12-37% reduction of energy use over conventional control methods without affecting thermal comfort and IAQ.

Structure-preserving model reduction of nonlinear building thermal models

Abstract This paper proposes an aggregation-based model reduction method for nonlinear models of multi-zone building thermal dynamics. The full-order model, which is already a lumped-parameter approximation, quickly grows in state space dimension as the number of zones increases. An advantage of the proposed method, apart from being applicable to the nonlinear thermal models, is that the reduced model obtained has the same structure and physical intuition as the original model. The key to the methodology is an analogy between a continuous-time Markov chain and the linear part of the thermal dynamics. A recently developed aggregation-based method of Markov chains is employed to aggregate the large state space of the full-order model into a smaller one. Simulations are provided to illustrate tradeoffs between modeling error and computation time.

Energy-efficient control of an air handling unit for a single-zone VAV system

A large fraction of energy consumed by the HVAC (heating ventilation and air-conditioning) system in a commercial building is consumed at the AHUs (air handling units) that condition a mixture of outside and return air to specific temperature and humidity levels. Traditionally, the return air ratio and temperature of the air leaving the cooling coils in the AHU (conditioned air temperature) are maintained at pre-determined set points instead of being based on real-time measurements of occupancy, zone humidity, and outside weather. In this paper, we investigate the potential of energy savings as a function of the complexity of control algorithm. The inputs that can be commanded by the controllers are: air flow rate, return air ratio, conditioned air temperature, and the temperature of air leaving the heating coils in the AHU. Simulation results show that the controllers that use the measurements of occupancy, zone humidity, and outside weather result in significant savings over conventional controllers that do not use such measurements, without sacrificing thermal comfort or indoor air quality (IAQ). Surprisingly, a simple feedback control scheme is found to perform almost as well as a much more complex MPC (model predictive control) controller.

Experimental evaluation of occupancy-based energy-efficient climate control of VAV terminal units

Results are presented from a nearly week-long experimental evaluation of a scalable control algorithm for a commercial building HVAC system based on real-time measurements of occupancy obtained from motion detectors. The control algorithm decides air flow rate and amount of reheat for each variable air volume terminal box based on real-time measurements of occupancy and space temperature. It is a rule-based controller, so the control computations are simple. The experiments showed that the proposed controller resulted in 37% energy savings over baseline on average without sacrificing indoor climate. In contrast to prior work that reports energy savings without a careful measure of the effect on indoor climate, it is verified that the controller indeed maintains indoor climate as well as the buildings baseline controller does. This verification is performed from measurements of a host of environmental variables and analysis of before/after occupant survey results. A complete system required to retrofit existing buildings with the controller is presented, which includes a wireless sensor network and a software execution platform. Two useful observations from this work are: (i) considerable energy savings—along with compliance with ASHRAE ventilation standards—are possible with simple occupancy-based control algorithms that are easy to retrofit; and (ii) these savings are attained with binary occupancy measurements from motion detectors that do not provide occupancy-count measurements. Results also show that there is a large variation in energy savings from zone to zone and from day to day.

COMMAND TRACKING IN HIGH PERFORMANCE AIRCRAFTS: A NEW DYNAMIC INVERSION DESIGN

Abstract This paper proposes a new straight forward technique based on dynamic inversion, which is applied for tracking the pilot commands in high performance aircrafts. Pilot commands assumed in longitudinal mode are normal acceleration and total velocity (while roll angle and lateral acceleration are maintained at zero). In lateral mode, roll rate and total velocity are used as pilot commands (while climb rate and lateral acceleration are maintained at zero). Ensuring zero lateral acceleration leads to a better turn co-ordination. A six degree-of-freedom model of F-16 aircraft is used for both control design as well as simulation studies. Promising results are obtained which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response and reduced control magnitude. Another advantage of this approach is that it leads to a significant reduction of tuning parameters in the control design process.