Passive House Sensor Networks: Human Centric Thermal Comfort Concept Mohd Izani Mohamed Rawi #1, Adnan Al-Anbuky *2 #
SeNSe Lab, Auckland University of Technology, Auckland, New Zealand 1 2
[email protected]
[email protected]
Abstract—The focus of this work lies on analysing Passive House (PH) requirement from human comfort perspective. The investigation will lead into plans for utilising Wireless Sensor Networks (WSN) as an enabling tool in human comfort solution. WSNs are used as cooperative smart objects that closely monitor living space’s environmental comfort (thermal comfort, visual comfort, indoor air comfort, acoustical comfort and spatial comfort), user preferences and energy consumption, manipulating appropriate Environmental Factors and Physiological Factors towards improving human comfort without sacrificing energy needs of a Passive House. The work concentrates on determining the thermal comfort of a living space where mobile node (occupant) and static nodes (environment) work together in calculating thermal comfort Predicted Mean Vote (PMV) value. This paper presents Java based SunSPOT sensor node, embedded with sensing engine and PMV engine to determine thermal comfort of a living space. We also present interpolation based PMV engine that will be used to determine thermal comfort where no sensor present. The experiment and simulation results indicate that the proposed engines exhibit a great potential in providing solution for PH activity.
I. INTRODUCTION Wireless Sensor Networks (WSNs) enable fine-grained collection of sensor data about the real world, and promise to revolutionize our understanding of and interaction with our environment. WSN devices are small, communicate wirelessly, have limited power, are low-cost, and most importantly are embedded in the physical world through their onboard sensors and actuators. With the proliferation of pervasive computing in digital environment there is an increasing demand for the inclusion of WSN in broad areas such as building, utilities, industrial, home, shipboard, and transportation systems automation and much more [1]. Sensory data comes from multiple sensors of different modalities in distributed locations. The smart environment needs information about its surroundings as well as about its internal operations. With more powerful WSN being introduced, more intelligence can be embedded into WSN itself, such as fuzzy logic based smart routing, swarm behaviour, ant colony optimisation and so forth. More intelligence is being embedded into WSN. System that unites pervasive or ubiquitous computing, (cognitive) intelligence and software-intensive systems is considered as 'embedded intelligence', or 'smart systems' from 'smart environments' broader context. It posed an
important challenge for strategic, long term research, with a huge impact on society and the economy [2]. A. Background Sensor networks are the key enabling technology for building systems that adapt autonomously to their environment, without direct human intervention. Most sensor networks operate in free air, but research being conducted in Ireland, between the School of Computer Science and Informatics at UCD Dublin and the Centre for Adaptive Wireless Systems at Cork IT, is starting to explore the tools and techniques we need in order to build 'augmented materials' which combine sensing, actuation and processing into the fabric of built objects [3]. In this work, WSN is evaluated as an enabling instrument to determine thermal comfort of a living space. The thermal comfort value is one of key element in determining environmental comfort that will drives to the solution for human comfort. The ultimate objective of this work is to provide solutions to human comfort predicament of Passive House by means of embedding Ambient Intelligence (AmI) technique into WSN. B. Related Work A number of separated studies have been conducted in various discipline that focus on specific issues and challenges associated with smart living space, ubiquitous computing passive house, and environmental comfort [4–7], [10–13]. These researches were done in their own specific domain needs. Within the smart living space and ubiquitous computing community, a number of studies have focuses on using WSN as a tool to recognise human activity based on actor’s interaction with everyday objects [10], [13]. On the other hand, numerous works from passive house and environmental comfort community concentrates on building a high efficiency low energy living space [4–7]. A myriad of low energy building approaches were look into. Yet, with all those high tech building approaches, they still use conventional separated / individual wired monitoring and management equipments such as conventional Heating, Ventilation, and Air-Conditioning (HVAC) system, conventional power / utility logger and manual adjustment of living space fixture such as opening / closing a blind. We believe that due to nature’s complexity and interrelated, any narrowly focussed research on a single conundrum can
not by itself solve the real world problem and it might introduce another problem outside its domain. Our work recognises this predicament and introduces the concept that unites discrete various research domains under one roof. C. Motivation The ultimate ambition of our work is in the research and development of sensor networks as enabling tools for monitoring and maintaining Passive House environmental comfort whilst minimising the energy usage and at the same time adjusting to the need and requirement of the user. Our primary motivation in this first stage is to develop deep understanding around challenges and needs in deploying low power, data intensive wireless sensor networks in Passive House environment. These environments are typically composed of an area with extensive data monitoring (as frequent as 10 minutes interval sampling rate) and real time multi sensor parameters and actuators. In order to fully develop the system, we must fully quantify the environmental comfort parameters. Our key contributions are: Analyse the requirement of human comfort and attempt at maximising the role of the sensor in accommodating for the related intelligence towards human comfort solution. Design and development of WSN based thermal comfort engine. Full evaluation of calculating / determining thermal comfort PMV value of a given living space. II. PASSIVE HOUSE SYSTEM OVERVIEW Most of earlier and recent effort in Passive House field employed a high-cost wired platform that separately monitors and managing all Passive House parameters [4–7]. The emergence of wireless technologies and miniaturisation of devices make it possible to implement the same functionalities for Passive House with less cost, better deployment time and improve integration between sub-systems. In particular, sensor nodes coupled together with actuators will be the building block of Passive House System Manager that are capable of monitoring various parameters, reasoning about the situation and occupant needs, and reacting to the computed information accordingly. Thus, eventually the Passive House activity will be filled with different type of sensors that can provide an assortment of context information related to themselves, the occupants, and other appliances such as energy consumption, and energy generations. A. Architecture Overview Fig. 1 illustrates a hypothetical context aware Passive House from energy centric activity point of view. The passive house system shall focus on the energy where power management sub system will work in tandem with climate control sub system, sensors, actuators and energy supply sub system whilst maintaining optimum energy usage based on passive house energy specification. Minimising energy usage is the primary goal yet retaining a high degree of environmental comfort. Various energy centric sensors such
as heating element power consumption, hot water power consumption and ventilator power consumption are placed through out the house.
Fig. 1. Energy Centric Activity: Energy Optimisation
Fig. 2 illustrates the human centric activity perspective where automation, personalisation and adaptation are the primary goal. When the occupant of passive house enter the living space, the sensors and actuators shall sprung into action to provide optimum environmental comfort based on occupants preferences and profile whilst maintaining minimum energy use according to passive house specification.
Fig. 2. Human Centric Activity: Automation, Personalisation, and Adaptation
The tight integration and interaction of the sensor nodes / actuators and the environment shall provide better experience in Passive House activity domain. B. Passive House System Manager Fig. 3 illustrates the building block of Passive House System Manager from software point of view. Environment Comfort sub system will give the indicator of a living space comfort level based upon Thermal Comfort, Visual Comfort, Indoor Air Comfort, Acoustical Comfort and Spatial Comfort output. Coupled with Occupant Preferences, Environmental Comfort sub system will learn and adapt to the need of the occupant. At the same time, PH Manager will accommodate the need through Actuator Control whilst maintaining energy goal of the passive house. Each of comfort sub system will be work out from sense parameters such as air temperature, mean radiant temperature, relative humidity, air velocity, clothing, metabolic rate, illuminance level, shading level, CO2 concentration and sound level. Due to modular comfort model, each comfort
factor can be calculated within the group itself even though the sense value might come from an integrated multi sensor node.
Where (2) (3) (4) The parameters are defined as follows: M: W: Icl: fcl:
Fig. 3. Passive House System Manager
Principally, the system will sense the environment, rank the environment, obtain the occupant need, get the energy usage and put into action the corresponding adjustment needed to achieve the desired goal. C. Thermal Comfort Environmental comfort: A collective sensory experience for a human that he / she feel pleasant and at ease with the surrounding. It encompasses thermal comfort, visual comfort, indoor air comfort, acoustical comfort and spatial comfort. Thermal comfort is considered as the most influential factor in determining environmental comfort [14]. This paper presents detail analysis in regard of thermal comfort operation with WSN. Thermal comfort is a condition of mind which expresses satisfaction with the thermal environment [8]. A human being's thermal sensation is mainly related to the thermal balance of his or her body as a whole. This balance is influenced by the environmental parameters as well as physiological parameters. Environmental factors include Dry Bulb Temperature (DBT), Mean Radiant Temperature (MRT), Relative Humidity (RH) and Air Movement (Vel). On the other hand, physiological factors consist of two factors namely the person’s Metabolic Rate (Met) and Clothing Level (Clo) [9]. When these factors have been estimated or measured, the thermal sensation for the body as a whole can be predicted by calculating the Predicted Mean Vote (PMV). PMV values could be calculated by:
(1)
ta: tr: Va: Pa: h c: tcl:
metabolism external work, equal to zero for most activity thermal resistance of clothing ratio of body’s surface area when fully clothed to body’s surface area when nude air temperature mean radiant temperature air velocity partial water vapour pressure convectional heat transfer coefficient surface temperature of clothing
There are seven levels of the PMV ranging from -3 to +3, which represents cold and hot respectively. When PMV is equal to zero, it implies a neutral and comfortable state. The association between the PMV value and its meaning is shown in Table I. TABLE I PMV VALUE AND ITS ASSOCIATED MEANING
PMV Value +3 +2 +1 0 -1 -2 -3
Meaning Hot Warm Slight Warm Neutral Slight Cool Cool Cold
III. THERMAL COMFORT OPERATION, SIMULATION AND RESULTS The first phase of Passive House System focuses on thermal comfort function within WSN. The hardware platform for all nodes is based on the SunSPOT wireless sensor platform. The SunSPOT uses 180MHz 32-bit ARM920T core processor with 512K RAM and 4M Flash and it is programmed almost entirely in Java. SunSPOT comes with 2.4GHz radio with an integrated antenna on the board. The radio is a TI CC2420 (formerly ChipCon) and is IEEE 802.15.4 compliant. Fig. 4 shows SunSPOT free range nodes and SunSPOT base station (with attached USB cable) used for measuring real time air temperature and PMV value of SeNSe lab.
Fig. 4. SunSPOT free range node and SunSPOT base station
A. Single Node PMV In this study, the SunSPOT sensor node is used to measure SeNSE lab’s air temperature and at the same time, PMV value is calculated. SunSPOT sensor node is programmed to measure the air temperature using built in temperature sensor, calculate PMV value and transmit the sensed air temperature, calculated PMV value and stamped the data with date and time to central PC via SunSPOT base station. Fig. 5 and Fig. 6 show SunSPOT’s air temperature measurement (DBT) and calculated PMV value. Real-time measurements were taken at 10 minutes interval started from 7th July 2009 6:39pm until 9th July 2009 4:49pm. In this experiment, it is assume that: 1) Mean Radiant Temperature (MRT) is equivalent to Dry Bulb Temperature (DBT) 2) Air Movement (Vel) = 0.1 3) Relative Humidity (RH) = 60% 4) Metabolic Rate (Met) = 1 5) Clothing Level (Clo) = 1
Fig. 5. Air Temperature in SeNSe Lab
Fig. 6. PMV Value in SeNSe Lab
Fig. 5 shows air temperature changes throughout the day. Air temperature fluctuation can be seen here due to central heating operation in the lab. During day time, air temperature climbs up to 270C. During night time, the central heating system was turn off, hence the drop of air temperature. Furthermore, Fig. 6 shows calculated PMV value that ranges from -0.61 (slight cool) to 1.11 (slight warm). From this observation, it can be presume that the thermal comfort in the lab is somewhat well-off since the PVM value never goes beyond -1 and 1. We observed that the sensing engine and PMV engine (PMVThermalComfort Midlet) embedded in SunSPOT consume only 4677 bytes out of 4M SunSPOT Flash memory. During the test run, we also observed that there is 312,208 bytes of free memory available out of 459,264 bytes available SunSPOT RAM. Available free RAM also varies a little bit due to internal Java Virtual Machine’s (JVM) garbage collection process. On average, SunSPOT spends about 200ms in active mode for sensing, calculate PMV value, compose a datagram and transmit the datagram to SunSPOT base station. Out of nearly 46 hours test run, SunSPOT only spends about 55,400ms in active mode, where as the rest of the time, it is in deep sleep mode. The PMVThermalComfort Midlet embedded in SunSPOT comprise of 2 classes: main class PMVThermalComfort.class and PMV.class that do the PMV value calculation. From operation point of view, every 10 minutes, PMVThermalComfort.class will: 1) Acquire the time stamp (current date and time) 2) Read air temperature sensor value (in Celsius) 3) Calculate PMV value (done by PMV.class) 4) Shows PMV value on SunSPOT onboard LCD 5) Prepare datagram () 6) Transmit the datagram to SunSPOT base station
7) Put SunSPOT nodes into deep sleep mode for 10 minutes to conserve energy
2) Compute the weight of each point (λ1, λ2, λ3 … λn). Weighting function is the inverse power of the distance.
B. PMV of a Given Living Space The Inverse Distance Weighted (IDW) interpolation technique is used to determine the Environmental Parameters that are used to calculate PMV index value of a person in a living space at a particular location. IDW is a deterministic estimation method whereby values at unsampled points are determined by a linear combination of values at known sampled points. IDW assumes that each point has a local influence that diminishes with distance.
(5) 3) Compute the weighted average for each Environmental Parameters ( , , and ). (6) 4) Compute Thermal Comfort PMV value at Physiological Parameters node N0. Table II summarises the calculation results of a typical living space simulation with 4 Environmental Parameters nodes and the distances between N0 and N1, N2, N3 and N4 are 3.04m, 1.58m, 2.24m and 3.64m respectively. Calculated PMV value at N0 is equal to -0.16 (Neutral). TABLE II PMV VALUE AT LOCATION N0
Fig. 7. Schematic Diagram of a Living Space
Thermal Comfort Parameters DBT MRT RH Vel Met Clo
Nodes N1
N2
N3
24 27 50 0.1 -
23 25 57 0.1 -
21 22 20 21 60 54 0.2 0.2 PMV at N0 =
N4
N0 22.50 23.41 55.94 0.14 1 1 -0.16
The IDW interpolation engine was further investigated with various random locations to determine the PMV values.
Fig. 8. PMV of a Living Space
Fig. 7 and Fig. 8 illustrate the experimental design of a living space. The following assumptions are taken into consideration while deploying the nodes into the model: 1) The locations of each Environmental Parameters nodes deployed in the simulation environment know its coordinates (x, y) and placed at 1.1m to 1.6m above floor level. 2) The Environmental Parameters nodes are stationary and Physiological Parameters node is mobile. We envisage a person fitted with Physiological Parameters node (N0) entering the living space. The sensor nodes cloud will: 1) Compute the distances to all Environmental Parameters nodes (d1, d2, d3 … dn).
Fig. 9. PMV Value at Various Locations of a Living Space
Fig. 9 shows PMV value at various location of a living space as shown in Fig. 8. From this simulation, one can seek
out for the most comfortable spot in a living space and the sensor nodes can recommend to the occupant the best spot from thermal comfort point of view. The spot with worst thermal comfort also can be identified and the sensor nodes can recommend to the living space environment sub system to make the necessary adjustment to overcome the predicament. IV. DISCUSSION AND FURTHER WORK We have described and evaluated the thermal comfort operation using SunSPOT sensor nodes. We observe that SunSPOT sensor node is computationally capable of acquiring raw measurement and at the same time calculates PMV value from equation 1. Although equation 1 is computationally complex, SunSPOT ARM based processor is capable enough to do the calculation. We also observe that SunSPOT battery life is not as good. After running for more than 46 hours with data transmission every 10 minutes, the battery drops from 100% to 85%. More work is needed to optimise the sensor power consumption such as datagram compression, better transmission scheduling strategy, dynamic antenna power management and more aggressive deep sleep mode to conserve power. Unnecessary onboard apparatus such as accelerometer, LEDs, general purpose I/O pins and switches can be power off to further reduce SunSPOT node power consumption. We need the nodes to adaptively reduce their workload to maintain continuous operations thus providing necessary data for Passive House System operations. On the simulation of PMV in unknown location, the engine shows a promising capability in determining thermal comfort where no sensor node is available. More works is needed in term of managing acceptable error rate that includes location engine error, IDW interpolation engine error and rounding calculated value of PMV error. With minimum error, better thermal comfort can be achived. More work also needed in determining the optimise sensor node number to be deployed for a living space. The relationship between living space size, number of sensor nodes to be deployed and accuracy of IDW based values is yet to be further investigated. On the whole, the works shows a considerable potential in utilising WSN as an enabling tool in human comfort solutions. As we moved forward the next phase of our work, this investigation will lead into plans for utilising WSN for assisting in human comfort overall solution. Our ultimate aim is to analyse Passive House requirement from human comfort perspective and eventually provide necessary solution for it. REFERENCES [1] D. J. Cook and S. K. Das, Smart Environments: Technologies, Protocols, and Applications. New York: John Wiley, 2004. [2] E. Schoitsch and A. Skavhaug, "Embedded Intelligence," ERCIM News, Issue 67, pp. 14-15, October 2006. [3] S. Dobson and K. Delaney, "Embedded Intelligence," ERCIM News, Issue 67, pp. 38, October 2006
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