978-1-4244-9730-0/10/$26.00 ©2010 IEEE

On Adaptive HVAC based on a De-Centralized Algorithm using K:1 Transmission Protocol for

Autonomous Wireless Sensor Network

Addwiteey Chrungoo1, Akshay Kumar, Pradeep Kumar, Jai Parkash Godara4 Department of Electronics and Communication, Amity University

Noida, India 1addwiteeyc@ase.amity.edu

4jprakash@amity.edu

Abstract – This paper presents the design of an adaptive HVAC based on psychrometric analysis and a De-Centralized control Algorithm for an Autonomous Wireless Sensor Network. The proposed system aims to initially differentiate the area under consideration into active and inactive zones and perform control operations on them. The De-Centralized control methodology follows a K:1 transmission protocol for optimized K-Coverage by allowing each independent node to act as a controller and send the instructions to the actuators directly without going through the central controller in the area under consideration. The K:1 protocol however does enable interaction of independent nodes with the central controller for central ‘air-distribution chamber’ valve actuations. The sensory nodes in the inactive zones remain dormant till the zones are identified as active. The control methodology introduces an optimized air distribution, a variable air volume and humidity analysis parameter into the HVAC Control. The approach is to establish the wireless link using wireless UART, thus allowing real-time monitoring, measurement and control by employing the Texas Instruments 2.4 GHz Radio-Frequency CC2500 module. Keywords – Wireless sensor network, Heating, Ventilating and air-conditioning, De-centralized Control, Psychrometry.

I INTRODUCTION

Conventional HVAC systems are known to consume a large percentage of the entire energy consumption of a building. Approximately 50% of the total energy consumption in domestic and industrial applications is due to extensive use of HVAC systems [1]. However, effectively managed control systems can reduce the energy consumption on account of HVAC by 40% [2].

Conventional HVAC works on the basis of generating air called mixed air; mixed air being the mixture of return air and outside air. The mixed air is then passed through coils to heat or cool the air on the basis of desired requirements. Cooling methodologies also include using chilled water from a chiller to cool the air. The conditioned air is then passed to the various distribution boxes from where further distribution takes place [3]. A block diagram of a single zone HVAC is shown [4] in Fig. 1.

With an aim to optimize energy consumption and improve air quality, various systems have been developed to monitor the functioning of HVAC [5-9]. Hussan [10] followed an embedded systems approach utilizing sensors, actuators along

with a keyboard and an LCD to serve as the user interface. On the other hand, Popa et al [11] developed a system for remote monitoring of temperature in the area outside the duct systems. While Lee and Dexter [12] developed a “fuzzy sensor” for measurement of temperature of the air leaving the mixing-box of the air handling units, Kwon et al [13] and Guo & Zhou [14] have described a system for distributed monitoring of air temperature in HVAC systems. Even though complex algorithms have been used for wireless sensor networks [15-17], their incorporation into adaptive and responsive HVAC has been minimum [18].

This paper proposes a control methodology for optimization of a traditional HVAC system. The proposed system follows a Decentralized control algorithm using a K:1 transmission protocol for developing an adaptive system. The system uses an autonomous wireless sensor network which functions differently from conventional centralized WSN’s by following broadcasting technique for point to point communication through a file of nodes while maintaining isolation from parallel nodes. The main features of our system are: • Quantization of air into air packets for optimized variable

air volume HVAC control and air distribution involving dampers.

• Decentralized nodal control. • Unique Transmission algorithm involving K:1 approach • Stratification of ‘Degree of cooling’. • Optimization of Requisite Humidity levels for the user.

II OPTIMIZED SYSTEM STRUCTURE

The HVAC system structure has been modified from the conventional commercial HVAC in order to attain optimized levels of air distribution and energy consumption. The proposed design introduces independent distribution boxes and parallel secondary ducts for each unit area. While air flow into the secondary ducts is controlled and channeled using the switching valves, variation in humidity is incorporated using chiller valve actuations which have been provided independently for each unit area.

A detailed block diagram of the HVAC system structure

has been shown in Fig. 2.

Fig. 1 Model of single zone HVAC System

The entire systems can be divided into a:

• Central Controller • Central control zone valve actuators • Distribution boxes • Fans • Chiller piping and Valves • Dampers • Temperature, Humidity and Velocity Sensors

The central controller works independent of the local controllers and is responsible for only the central air distribution valve actuations. The ductwork remains closed if air distribution is not required and remains open if the same is required by the corresponding rooms. Fan speed is kept variable depending upon control parameters explained in the control algorithm section. The chiller valves and the dampers are also actuated depending upon the feedback from the external, internal and duct sensors involved in the control process. The system structure is as follows: A. Wireless Nodes

The wireless sensor nodes have been classified under two different heads: the sensor nodes and the Transmission nodes.

1) The Sensor Nodes: The sensor nodes are used in the system to provide feedback to the controllers for optimized control. They have been further classified under three different groups. Group one sensor nodes have been installed inside the rooms. They include sensing elements that measure the room temperature, the room humidity levels and the population index function values. Within this group of nodes, a user interface has been provided to the user to set the desired effective room temperature along with humidity levels in the room. Group two sensor nodes have been installed outside the building near each room. These nodes have been installed to sense the outside air temperature. The outside temperature is used to calculate the room temperature according to a pre set equation. The group three sensor nodes are installed in the ducting of the HVAC system. The nodes include sensing elements used to measure the humidity levels

in the ducts carrying the cool moist air and the velocity of the air flow.

2) The Transmission nodes: The transmission nodes form the communication framework of the entire control system. They have also been divided into two different categories of wireless nodes. The group one nodes are termed as the cluster heads. Each cluster head is responsible for a set of four rooms. The main function of the cluster heads is to transmit whether the room is classified as active or inactive. The data is represented by high and low signals which are forwarded to the group two nodes for further transmission. The cluster heads use the broadcasting algorithm rather than a packet transfer for point to point communication. The group two includes multiple file of nodes used to transfer the active or inactive status of the rooms to the main controller. These nodes also follow the broadcasting algorithm for intra group communication within the same specified channel.

B. Actuators

The actuators are used to control the HVAC system valves and dampers. The actuator actions result in modulation of the fan speed thereby the volume of air supply and the humidity levels. They have also been classified into two groups namely:

• Central Actuators • Local Actuators

1) Central Actuators: The central actuators control the

central air distribution valves which thereby affect optimized level of air distribution in the entire ductwork. The central actuators also control the speed of the AHU and thereby modulate the air flow supply [18]. The supply air temperature however is kept constant throughout the process [4].

2) Local Actuators: The local actuations are the main

actuating systems in the proposed concept as the entire concept is De-Centralized in nature. Local actuators control the damper angle, which thereby modulates the volume of air entering the room from the duct. The fan speed and chiller valve actuators are also considered under the local actuators group. Modulation of these actuators modifies the velocity and volume and humidity of air passing through the system.

Fig. 2 The block diagram of the HVAC system structure

Air flow into the secondary ducts is controlled and channeled using the switching valve actuators. Modification of the switching valves determines the direction of air flow after the distribution box stage. This use of directional air flow reduces the wastage of cool air as air passes only to rooms considered active, thereby decreasing energy consumption. C. Energy Estimation – A theoretical analysis

There are two basic energy consumption components in the HVAC system. The efficiency of these components needs to be determined in order to ascertain optimization levels. The components under consideration are:

• Fan • Chiller

1) Fan: The proposed control algorithm modulates the fan speeds on the basis of a set of equations. The fans present in the distribution boxes supply cool air from the primary ducts to the secondary ducts for the set of 4 rooms, while the AHU fan supplies cool air from the main system to the primary ducting. Variation in the fan speed varies the amount of air being supplied to the primary and the secondary ducts. Modulating the fan speed in both the stages helps in increasing energy conservation as only requisite supply of air would hence be used for distribution. The energy consumption is calculated in [19] as follows:

∑1.2 2 10 ∑1.268 10 1

2) Chiller: The chiller is used to cool the air using water in the chilling system. However, in our control algorithm we have considered the supply air temperature as constant. The energy consumption is calculated in [19] as follows:

∑1.2 1 2 Where, λ is the outdoor air fraction in the supply air.

0.2, ∑1.2 3 1.0

Also, 0; 4 Hence, total energy consumption theoretically is given by 5 which is the sum of the Fan Energy consumption and the chiller energy consumption. Where: Et is the Total energy consumption; Ef is the Fan energy power consumption; EC is the Chiller energy consumption; qS is the Indoor sensible load

(kW); Tr is the Zone room temperature (oc); TS is the Supply air temperature (oc); PS is the Static pressure (pa); hO is the Enthalpy in outdoor air (kJ/kg); qt is the Total load of building (kW) and COP is the Coefficient of chiller performance

III THE NOVEL DE-CENTRALIZED CONTROL ALGORITHM

In order to support the system structure as defined, we

follow a three stage control algorithm. Each control stage is dependent on the other, however, adaptive control is performed independently by each stage as only a chain of input data from previous stage is required for the current stage to pass instructions as per the algorithm. The entire framework is divided into the following three zones:

• The Rooms and corresponding ducting in the room • The distribution boxes provided with local fans and

chiller pipes. • The central ducting network and main fan.

A. Stage one Control

Our primary control is performed in the rooms and corresponding ducting in the room. Each room in a set of four rooms are equipped with Passive Infrared motion sensors, the sensors classify the rooms as active if population index function is greater than zero or inactive if the PI function is equal to zero. If the zone is classified as active, the temperature and humidity sensors inside the rooms start transmitting the data to the room Controller. The controller then performs adaptive control by allowing the ducts to transmit discrete packets of fixed volume of cool air to reduce the room temperature to desired level. The algorithm calculates according to psychrometric analysis, the volume of cool duct air required to be emitted. The total required volume of air is then emitted from the duct in discrete packets. The ducting is equipped with chiller pipes which allow the user to have a desired humidity level to reduce dryness during dry summer seasons. The stage one control is dived into:

1) Temperature control: As mentioned above, we have

used psychrometric analysis to determine the volume of air required to cool the room at a particular temperature to a desired level. The analysis uses the concept mixing humid air as shown in Fig. 3.The duct air mixes with the room air and the resultant air is modified in terms of temperature and humidity. Temperature control is met by the following equations: 6 Now, on modifying (6) and assuming that the mix does not produce any fog, we get: 7 8

Fig. 3 Figure representing the mixing of duct air and room air

On Transforming (8) we get: 9

Now, here is the final volume of air after mixing. However, we need to determine the volume of duct air required to cool the temperature. Hence, we get: 10 The nomenclature for (6)-(10) is shown in Table 1.

The effective room temperature is theoretically contributed by the outside temperature, temperature through the walls, glass, heat from appliances and heat due to human activity. Since incorporation of all these parameters increases complexity, the room temperature has been measured as a lump sum.

2) The humidity control: The humidity control is met by

use of the following equations: 11 where is the humidity ratio in the room, is the required humidity ratio in the duct and is the desired humidity ratio after mixing of air.

Now on transforming (11), 12 The humidity control algorithm is set such that when the required humidity level would be input, the controller would calculate the required humidity to be maintained in the duct so as to attain the desired humidity level. Hence, 13

XD is to be determined by the controller on its own. However since humidity ratio is not directly measurable we determine the humidity ratio from the partial pressure of water vapor. 14

In (14), X is the humidity ratio, ew is the partial pressure of water vapor, Mw is the molar mass of water vapor (18.02 kg/kmol), MD is the molar mass of dry air (28.97 kg/kmol) and Pa is the bulk pressure of parcel of moist air.

Relative Humidity can obviously be determined by use of sensors, hence, we use the below mentioned equation to determine the partial pressure of water paper.

TABLE 1 NOMENCLATURE

Symbol Parameter Denoted Volume of air in room initially (m3) Specific Enthalpy of moist air in room (Kj/Kg) Volume of air in duct (m3) Specific enthalpy of moist air in duct (Kj/Kg) Final volume of air in room after mixing (m3) Final specific enthalpy after mixing (Kj/Kg) Sp. Heat capacity of air at constant Pressure Initial Room temperature (oC) Duct air temperature (oC) Required temperature after mixing of air (oC) Volume of duct air required (m3) % 100% 15

Hence, 100 16 where is the partial pressure and is the saturated vapor pressure of water at prescribed temperature. The attained partial pressure values are used in (14) for determining XREQ and XR which are then used in (13) to determine the required humidity ratio for the duct. The saturated vapor pressure of water can be calculated using the Arden-Buck equation: 1.007 3.46 10 6.1121 .. 17 Where, P is the absolute pressure (mbar) and T is the dry bulb temperature (oC) and is in hectopascals (hPa). In our algorithm however, we have pre-calculated the saturated vapor pressure of water and fed into the controller memory to reduce complexity. B. Stage two Control

In the system structure, we have allotted a Local Controller for a set of four rooms. This local controller performs the function of obtaining information from the Passive Infrared sensors to ascertain the occupancy states of each of the four rooms. There is a single distribution box for the set of four rooms. On the basis of the occupancy patterns of each of the four rooms the second stage control is performed by varying the air flow from the distribution boxes and by keeping the valves closed for the rooms depicting an inactive state and opened for the rooms depicting an active state.

We have proposed a ‘grade of cooling’ function whose values determine the air flow of the fan from the distribution boxes. 18

In (18) ‘n’ is the Room number in the set of four rooms, is the population index factor determining the extent of cooling

required and is the classification of the room as active or inactive. Greater the value of , higher would be the air flow.

C. Stage Three Control

The stage three control focuses on the channeling of air in the primary ducts using effective closing and opening of valves as per requirement and modulating the primary fan speed on the basis of load. A probabilistic model can also be formed to determine the frequency of switching in the network. IV TRANSMISSION PROTOCOL

The communication is divided into two different modes. One half of the communication takes place in a Broadcast mode while the other half takes place in packet mode. A sample topology and communication system showing the k:1 transmission is depicted in Fig. 4. As per algorithm, entire data is not required to be sent to the central processor as control activities are divided into stages. The central processor’s job is to analyze the active and inactive zones and then effectively channel the duct air on the basis of load requirements. Initially every node is in sleep mode. As soon as the PIR sensor senses an activity, it notifies a change in status of the room from inactive to active. The notification is sent to the Room controller via broadcast mode. During broadcast mode data is actually transmitted to each and every node in the coverage, however, the nodes have been programmed to receive data only from specified pathway nodes which possess the same channel number. Hence, data transmission takes place in an error free manner and inputs are not lost during communication. The room controller then notifies the information to the local controller in the distribution box. This transmission however takes place using the Packet transfer mode as the local controller needs to identify the source of the information. Along with data, coordinates of the room controller are sent to the Local controller. The room controller on the basis of the inputs uses the grade of cooling function and then modulates the air flow. This notification of air requirement is also sent to the central processor so as to channelize the air. Communication between the central processor and the local controller takes place through the cluster heads and their file of nodes. Each cluster head transmits data through its file of nodes in the broad cast mode. As specified earlier, data is received only if the node recognizes the transmitter as part of its pathway. The last node in the sequence then switches to the packet mode again so that the central processor can identify the co-ordinates of the original transmitting node and the corresponding data input. In this manner, the transmission uses K different pathways for each cluster head and terminates by switching to the packet transfer mode in the end. The transmission is therefore called the K:1 transmission as pathway is predetermined by the originating local controller and its cluster head. The last stage of the transmission uses Time Division Multiplexing to transmit the inputs and coordinates to the central processor node wherein the last node of the K1 pathway notifies the K2 pathway that

its transmission is over. The K2 informs the K3 and so on till all pathway transmissions are complete. The total time taken is divided into rounds and interval between each round is kept substantially large in order to conserve energy.

Fig. 4 Topology and Communication System

V NUMERICAL RESULTS AND SIMULATION

Simulations to evaluate the proposed system were performed on MATLAB. Data regarding volume of cool duct air required and humidity requirements were attained and have been shown in the plots below in Fig. 5-6 while Table 3 shows the numerical results attained after simulations. The readings of the variation in humidity levels which were attained over a period of 24 hours during summer have been plotted as a graph in Fig. 7. The humidity requirement as per the user would be determined as function of current humidity and would always be calculated relative to the humidity level at the time of operation of the entire system.

Fig. 5 Plot of Average temperature readings with time and the Volume of

cool air required VI CONCLUSION

In this paper we have proposed the development of an adaptive HVAC control mechanism using mixing of air

streams according to psychrometric analysis. The calculation of volume of cool air required to reduce room temperature to desired levels and also set optimum humidity levels has been proposed. Since duct size is fixed, hence it was suggested to allow discrete packets of air to be emitted so as to reduce room temperature as per desired levels. Simulations have been performed for specific constraints as per weather conditions during the testing period.

TABLE 3 NUMERICAL READINGS OF AIR REQUIREMENT

TREQ = 25oC TR VREQ TR VREQ TR VREQ TR VREQ 25 1.60 31 46.96 37 92.32 43 137.68 26 9.16 32 54.53 38 99.88 44 145.24 27 16.72 33 62.08 39 107.44 45 152.8 28 24.28 34 69.64 40 115.00 29 31.84 35 77.20 41 122.56 30 39.40 36 84.76 42 130.12

Fig 6 Variation in required volume of air with varying temperature in 24

hours

Fig 7 Variation of humidity over 24 hours

Fig. 8 Variation in load on system determined by grade of

cooling equation

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