Neal Master

Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control

Heating, ventilation, and air conditioning (HVAC) systems are an important target for efficiency improvements through new equipment and retrofitting because of their large energy footprint. One type of equipment that is common in homes and some offices is an electrical, single-stage heat pump air conditioner (AC). To study this setup, we have built the Berkeley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) platform. This platform allows us to actuate an AC unit that controls the room temperature of a computer laboratory on the Berkeley campus that is actively used by students, while sensors record room temperature and AC energy consumption. We build a mathematical model of the temperature dynamics of the room, and combining this model with statistical methods allows us to compute the heating load due to occupants and equipment using only a single temperature sensor. Next, we implement a control strategy that uses learning-based model-predictive control (MPC) to learn and compensate for the amount of heating due to occupancy as it varies throughout the day and year. Experiments on BRITE show that our techniques result in a 30%-70% reduction in energy consumption as compared to two-position control, while still maintaining a comfortable room temperature. The energy savings are due to our control scheme compensating for varying occupancy, while considering the transient and steady state electrical consumption of the AC. Our techniques can likely be generalized to other HVAC systems while still maintaining these energy saving features.

Identifying models of HVAC systems using semiparametric regression

Heating, ventilation, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we have built on the Berkeley campus for modeling and efficient control of HVAC systems, and we use these testbeds as case studies for system identification. The main contribution of this work is that the use of semiparametric regression allows for the estimation of the heating load from occupancy, equipment, and solar heating using only temperature measurements. These estimates are important for building accurate models as well as designing efficient control schemes, and in our other work we have been able to achieve a reduction in energy consumption on a single room testbed using heating load estimation in conjunction with the learning-based model predictive control (LBMPC) technique. Furthermore, this framework is not restrictive to modeling nonlinear HVAC behavior, because we have been able to use this methodology to create hybrid system models that incorporate such nonlinearities.

Energy-Efficient Building HVAC Control Using Hybrid System LBMPC

Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.

Power control for wireless streaming with HOL packet deadlines

We consider the problem of streaming media packets from a transmitter buffer to a receiver over a wireless channel, controlling the transmitter power. When each packet comes to the head-of-line (HOL) in the buffer, it is a given a deadline D, which is the maximum number of times it can attempt retransmission in order to get successfully transmitted to the receiver. If that fails to happen, the packet is dropped and the next in line packet becomes the HOL one. Cost is incurred in each time slot for keeping packets in the transmitter buffer, transmitting power, and dropping HOL packets exceeding their deadlines. We investigate how transmission power should be chosen efficiently given the remaining backlog and residual transmission attempts of the HOL packet, so as to minimize the overall cost to transmit the buffer content. We formulate the optimal power control problem and study properties of the optimal control, proving that it is monotone under certain conditions. We then develop an approximate power control, which increases logarithmically in the transmitter backlog, and is demonstrated to perform quite closely to the optimal one.

Adaptive Prefetching in Wireless Computing

In this paper, we consider a basic issue in wireless computing, where mobile devices (with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points over wireless channels of fluctuating quality. When the channel quality is low, slow data downloads can contribute to application latency, and degradation of the user experience. To mitigate this issue, mobile devices can prefetch data during good quality channel periods preemptively, in anticipation of using them during low quality channel epochs. In other words, under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively to reduce application latency when channel conditions have degraded. Considering channel fluctuations, memory constraints, and application latency, the issue at each point in time is whether to prefetch or not. A dynamic programming approach is taken here to determine the optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm, which uses a randomized approximation of FON, thus lifting the need for online optimization and substantially reducing the computational complexity. Simulations are used to demonstrate that our low-complexity schemes perform well when compared to the optimal. In addition, our schemes outperform benchmark design techniques in realistic channel conditions.

Service Rate Control For Jobs with Decaying Value

The task of completing jobs with decaying value arises in a number of application areas including healthcare operations, communications engineering, and perishable inventory control. We consider a system in which a single server completes a nite sequence of jobs in discrete time while a controller dynamically adjusts the service rate. During service, the value of the job decays so that a greater reward is received for having shorter service times. We incorporate a non-decreasing cost for holding jobs and a non-decreasing cost on the service rate. The controller aims to minimize the total cost of servicing the set of jobs. We show that the optimal policy is non-decreasing in the number of jobs remaining - when there are more jobs in the system the controller should use a higher service rate. The optimal policy does not necessarily vary monotonically with the residual job value, but we give algebraic conditions which can be used to determine when it does. These conditions are then simplified in the case that the reward for completion is constant when the job has positive value and zero otherwise. These algebraic conditions are interesting because they can be verified without using algorithms like value iteration and policy iteration to explicitly compute the optimal policy. We also discuss some future modeling extensions.

MYOPIC POLICIES FOR NON-PREEMPTIVE SCHEDULING OF JOBS WITH DECAYING VALUE

In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perform in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.

A Mathematical Model to Study the Dynamics of Epithelial Cellular Networks

Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution, we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).

An infinite dimensional model for a many server priority queue

We consider a Markovian many server queueing system in which customers are preemptively scheduled according to exogenously assigned priority levels. The priority levels are randomly assigned from a continuous probability measure rather than a discrete one and hence, the queue is modeled by an infinite dimensional stochastic process. We analyze the equilibrium behavior of the system and provide several results. We derive the Radon-Nikodym derivative (with respect to Lebesgue measure) of the measure that describes the average distribution of customer priority levels in the system; we provide a formula for the expected sojourn time of a customer as a function of his priority level; and we provide a formula for the expected waiting time of a customer as a function of his priority level. We verify our theoretical analysis with discrete-event simulations. We discuss how each of our results generalizes previous work on infinite dimensional models for single server priority queues.

An infinite dimensional model for a single server priority queue

We consider a Markovian single server queue in which customers are preemptively scheduled by exogenously assigned priority levels. The novelty in our model is that the priority levels are randomly assigned from a continuous probability measure rather than a discrete one. Because the priority levels are drawn from a continuum, the queue is modeled by a measure-valued stochastic process. We analyze the steady state behavior of this process and provide several results. We derive a measure that describes the average distribution of customer priority levels in the system; we provide a formula for the expected sojourn time of a customer as a function of his priority level; and we provide a formula for the expected waiting time of a customer as a function of his priority level. We interpret these quantitative results and give a qualitative understanding of how the priority levels affect individual customers as well as how they affect the system as a whole. The theoretical analysis is verified by simulation. We also discuss some directions of future work.