Abstract-This paper presents a conceptual design of a multizone hvac controller based on a wireless sensor network that aims to optimize energy consumption ...
The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON) Nov. 5-8, 2007, Taipei, Taiwan
Minimizing HVAC Energy Consumption Using a Wireless Sensor Network
Yahia Tachwali University of Oklahoma Electrical and Computer Engineering Department
ytachwaligou.edu
Hazem Refai University of Oklahoma Electrical and Computer Engineering Department
hazemgou.edu
Abstract-This paper presents a conceptual design of a multizone hvac controller based on a wireless sensor network that aims to optimize energy consumption while maintaining the comfort level of users. The proposed system is capable of learning the occupancy pattern of the residents in the building. Moreover, it is transparent (requires no manual configuration). Wireless sensor network technology is used to collect the required measurements for the control system. simulation results are used to demonstrate the system performance in terms of power consumption and comfort level. Additionally, a comparison between the proposed system and other hvac control systems is presented. The results show that the proposed system is capable of providing a major energy savings.
John E. Fagan University of Oklahoma Electrical and Computer Engineering Department
jfagangou.edu
how to configure the system to work according to their preferences. Moreover, these controllers are not adaptive. They adhere to the manufacturer's configuration and do not adapt to the customers changing schedule or other new activities that require reconfiguring the system to accommodate these updates. Therefore, many people end up using these programmable systems as a traditional thermostat with basic settings, thereby loosing the benefit of reducing their energy usage and having paid a premium for features they do not use. Installing a programmable controller on an existing HVAC system is one way to increase the efficiency of an existing system without upgrading to a new system. Even though, there are many installed HVAC control systems nowadays, many I. INTRODUCTION may not be utilized efficiently. The cost and time required to There is an increasing demand for Heat Ventilating and Air install a new control system is a critical factor in the success of Conditioning (HVAC) systems due to the increasing HVAC controllers. population and global warming and many other serious reasons. Our proposed controller system is designed to address all Energy conservation becomes a very important problem to be the problems previously stated. It aims to optimize the energy addressed as it affects the local and global quality of life. consumption of the HVAC system by enabling the conditioned Researches have made efforts to improve air quality air flow to run into only occupied areas. Moreover, the system produced by HVAC units [1-3]. However, to date there is no possesses an adaptive functionality that enables it to track the cost efficient commercial utilization of this technology. HVAC occupancy and activity pattern of the residents of a building. systems are large consumers of energy, resulting in high The propose controller logs occupancy data and uses it to electricity bills. HVAC systems consume around 5000 of the estimate occupancy load per area, which means that the system total electrical energy generated in the world [4]. Hence, it is will be able to predict residents movement in the building and very important to optimize the energy consumption of the manage the air flow to avoid sudden temperature changes. This HVAC system by maximizing energy utilization. Recent capability optimizes comfort levels for building residents. studies showed that commercial buildings could reduce energy The main contribution of our system is that it provides a usage between 15% and 400O by closer monitoring and cost efficient and adaptive HVAC control that: managing its energy usage [5]. The centralized control of * Utilizes wireless sensor technology to minimize installation HVAC system has an inherited weakness by allowing the or costs upgrading energy to be distributed equally around the serviced zones. This causes a considerable amount of energy to be wasted. * Tracks the occupancy patterns of people in the building to optimize the generated air flow Therefore, an intelligent HVAC control is required to minimize * Provides transparency, meaning it requires minimum user the energy consumption and optimize the comfort level. interference to install and operate Several studies have been conducted to optimize the energy * Can be customized using different set points for each of these consumption HVAC systems [6-9]. However, systems building zone. are not adaptive to user behaviors [6,7] or they are too complicated to be implemented in a simple embedded system if they are used for multizone buildings [8,9]. II. ANALYTICAL MODEL Although there are some advanced control solutions that The dynamics of zone temperature is described by a provides programmable configuration such as operation time lumped capacity model. This mathematical model is used to and multiple set points, these systems fail to achieve their main evaluate the performance of an HVAC system. The followings purpose, energy saving, for several reasons. First, these are the that were made to develop the model: assumptions programmable controllers require product education. The * The zone temperature distribution is homogeneous. limited interface associated with these systems drives the customers to read the product operating manual to figure out
1-4244-0783-4/07/$20.00 C 2007 IEEE
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* The zone walls and roof have identical effect on the zone temperature while the ground effect is negligible. * The density of the air is constant * Interzones' pressure losses are negligible. * External heat sources such as human bodies and appliances are simulated as an uncontrolled input. Under these assumptions, the dynamics of the zone temperature is described by (1-4). Table I lists the notations used in model equations. T=
(TSU -T)+2 (TW1
T., TW =-
-
I
(TW1 -T)- 7
Cw2
(TW T)
wl
T)2+A(TR-T)+ 2(5
wl
-
-
TW
CW2
G(R
I
q(t) Monitoring System
(TW1 -T,)
C
T RR (T 'R -T)- CR CR
-T)+
T
Fig. 1. The proposed HVAC control system block diagram
(3)
A. Monitoring System A monitoring system is a collection of wireless sensor nodes divided into two groups. The first group includes the zone sensor nodes which are installed at each zone. Each node contains sensing elements that measure: temperature, humidity and occupancy (using an infrared sensor). The zone sensor node is equipped with a simple interface to adjust the temperature set point at the associated zone. This set point will be sent with the measurement data that are sent periodically to the main controller. The second group in the monitoring system is the external sensor node. It is used to collect the external weather conditions. A light sensor in the external sensor node provides a feedback of the day's condition (day/night, cloudy/clear). The difference in temperature inside and outside the building is calculated to determine the air flow and flow duration to bring the zone temperature to the required temperature set point.
(4)
-T
where = fPCpa, y= UA1 ,A = UrAr ,3= UA2 Equation (1) describes the rate of thermal energy change in the zone. Equations (2-4) represent the rate of thermal energy change through walls and roof. By applying Laplace transform to (1-4), we can reach the transfer function of the thermal zone model shown in (5): T(s) GZ(s) [/, Ts.p(s)+2 Y GW1(s)[T(s)+T0(s)] + 2 6 Gw2(s)[T(s)+T0(s)]+ A GR(S)[T(s)+TO(s)]] (5) where 1 , GR(s)= GZ(S)= ( CGs2+S a
+ C CRS++A Gwl(s)= C.1s + 2r2
B. Actuators
Cw2 s+2d' III. HVAC CONTROL SYSTEM MODEL The proposed system consists of 3 main components: a main controller, monitoring system and actuators (see Fig. 1).
T
TABLE I NOMENCLATURE Zone Temperature [°C] Total thermal capacitance C of walls (east,west) [kJ/C] Total roof thermal Temperature of walls CR (north,south) [°C] capacitance [kJ/C] Air supply volume flow Temperature of walls f rate [m3/s] (east,west) [°C] Total wall heat transfer U Temperature of the roof coefficients [W/mThC] [°C] Total roof heat transfer Ur Temperature of air supply
T
External Temperature
T T
wl
Tw2 W2 TR
sup
Total
C C
[0C]
pa
Cwl
zone
[°C]
thermal
capacitance [kJ/C] Specific heat of air [kJ/C] Total thermal capacitance
of walls(north,south)[kJ/C]
A1 Ar
AI A /
q(t)
[W/m2oC]
coefficients Area of wall i and roof
Area of wall i and roof mr in2] Density of air [kg/m3] External uncontrolled heat sources [W]
Actuators
(2)
The actuation system is split into global action actuators and local or zone actuators. The global actions affect the temperature in all controlled zones. The first global action controls the speed of the air handler which directly affects the total air flow supply. The second is global action sets the heat/cool coil temperature that determines the steady state point of the zone temperature. The third global actions is initiated by the economizer controller which determines when outdoor air will be used for cooling based on the external weather measurements and how much outdoor air to use. The zone actuators are basically dampers that control the conditioned air flow to the associated zone. It is the main actuation element controlled by the main controller. C Main Controller The main controller is an adaptive programmable device that is mounted at the center of the controlled building. It is configurable; however, it can run without initialization or prior configuration except for the required temperature set points that can be entered directly to the main controller or remotely at each zone through the corresponding internal sensing node interfaces as well as by external node sensing weather conditions. The control process is based on the measurements and set point updates received from the monitoring systems. The
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controller logs the occupancy measurements to be used for future activity and occupancy estimation (load estimation) to avoid the discomfort that occurs when there are sudden changes in temperature while moving between zones. D. Control algorithm The controller aims to minimize the energy consumption by minimizing the generated air flow while maintaining an acceptable comfort level. As the system utilizes the wireless sensor technology, it is crucial to minimize measurement dissemination. The frequency of periodic measurement dissemination is determined by the occupancy of the zone. If a zone is occupied, the occupancy sensor will activate the sensor node and the sensor node will transmit measurements every 5 minutes. If the zone is unoccupied, the sensor node will be in the sleeping mode and will be activated by an inquiry message send every 10 or 15 minutes from the main controller. This inquiry period is determined by the main controller based on the expected future occupancy time. The controller determines the required air flow based on the "total occupancy value, the "TO" of the zone. The total occupancy TO is a combination of three parameters: TO =al.CO+a2.AO+a3.OP (6) Zone Flow= TO x Maximum Zone Flow (7) * CO is the current occupancy status of the zone. It reflects the number of people in the zone. CO is normalized by the maximum number of people that can occupy the zone. * AO is the adjacent room occupancy level. The existence of people in adjacent rooms increases the probability of the zone being occupied by these people in the future. Fig.2 shows the probability calculation based on the local occupancy measurements and neighboring zone measurements. * OP is the occupancy pattern value of each zone. It is the probability of the zone being occupied based on the historical occupancy measurements logged in the memory of the main controller. Fig. 3 shows a conceptual illustration of occupancy pattern change over time for different zones (or rooms). * at, a2, a3: are weighting factors that sum up to 1. Hence, the system control parameter is TO which gets its adaptive nature from OP and its prediction capability from OP and AO as well as the instant response to CO status.
Time
Fig. 3. Occupancy pattern as a function of time and zone (room)
IV. SYSTEM EVALUATION A simulation study was performed to evaluate the proposed HVAC controller performance compared to three other controllers. The simulation was composed of two main components: the multizone thermal model using the analytical model developed in Section II, and the HVAC controller. Parameter values in [10] were used in building the mathematical model. A. House Thermal Simulation Model The house is assumed to be composed of 8 rooms of identical size 1 Om x 4m x 4m. Each room is considered as a single zone. One room is assumed to be unoccupied in order to visualize the effect of occupancy in the performance of HVAC controllers. The model has three inputs, the external temperature, air supply flow commanded by the HVAC controller, and the zone occupancy state used as an uncontrolled heat source. Fig. 3 shows the block diagram model for a single zone (room). B. HVAC Controller The simulation tested the performance of 4 different types of HVAC controllers: * Single set point and global actions There is a global temperature set point for turning the air flow on and off for all the rooms. The HVAC will run once the temperature in one room rises above On threshold To., (300C) and will supply a fixed cooled air flow f max to all the room until the temperature at all rooms exceed the Off threshold Toff (250C). Hence, the flow generated by this controller is represented by (8). > Ton when fi = |max jE (8) 0 when max(T1) < Toff
max(T()
Fig. 2. The combinational probability distribution calculated from local and neighboring zone measurements.
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Fig. 4. Block diagram ofthe zone model
{1,2,...Nmax}
V. SIMULATION RESULTS The simulation results represent the performance of the 4 HVAC controllers under two different external temperatures. The first simulation was performed for a hot day (To=400C) and another simulation for moderately hot weather (TO=320C). Fig. 5-8 show the temperature behavior and the controller action (air flow) under a variable occupancy status for a hot day condition (24 hours). Fig. 5 shows over cooling in the f max when T, > / where i e {1,2,...N max} (9) room since the temperature went below the Toff set point. This 0 when T, < Toff condition occurs because the HVAC system in this controller * Zone basedprogrammable setpoints and zone based actions will not be turned off until all the rooms cooled to the Toff There are two temperature setpoints at each room threshold (250C). Therefore it is clear that using that control determined based on the occupancy condition of the room. The system creates a huge waste that increases dramatically as the flow is represented by (10). We chose Ton =30°C, Ton2=350C, number of zones increases. Fig. 6 shows a stable behavior Toffl=250C, Toff2=300C because the temperature and air flow control is performed for zone. However, waste appears during the period the room A=|ffmx (7Ti > Toni & Occupied)j (T7 > Ton2 & nonoccupi&1) each is unoccupied. Fig. 7 shows results using a programmable set 0° (Ti
