Indoor air quality and energy management through real time …epubs.surrey.ac.uk/809511/1/Kumar et...

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Kumar, P., Martani, C., Morawska, L., Norford L.K., Choudhary R., Leach, M., 2016. Indoor air quality and energy

management through real-time sensing in commercial buildings. Energy and Buildings 111, 145-153. [Online Link]

Indoor air quality and energy management through real–time sensing in

commercial buildings

Prashant Kumar1, 2,*

, Claudio Martani3, Lidia Morawska

4, Leslie Norford

5, Ruchi Choudhary

6, Margaret

Bell7, Matt Leach

8

1Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences

(FEPS), University of Surrey Guildford GU2 7XH, Surrey, United Kingdom 2Environmental Flow Research Centre, FEPS, University of Surrey Guildford GU2 7XH, Surrey, United

Kingdom 3Centre for Smart Infrastructure and Construction, Department of Architecture, University of Cambridge,

1–5 Scroope Terrace, Trumpington Street, Cambridge CB2 1PX, United Kingdom 4International Laboratory for Air Quality and Health, Queensland University of Technology, 2 George

Street, Brisbane, Qld 4001, Australia 5Department of Architecture, Massachusetts Institute of Technology, Boston, MA 02139, USA

6Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United

Kingdom 7School of Civil Engineering and Geosciences, Newcastle University, Claremont Road, Newcastle upon

Tyne NE17RU, United Kingdom 8Centre for Environmental Policy, Faculty of Engineering and Physical Sciences (FEPS), University of

Surrey Guildford GU2 7XH, Surrey, United Kingdom

Graphical abstract

*Corresponding author. Address as above. Tel. +44 1483 682762; fax: +44 1483 682135; Email addresses:

[email protected] or [email protected] (Dr Prashant Kumar)

http://www.sciencedirect.com/science/article/pii/S0378778815304023

mailto:[email protected]

mailto:[email protected]

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Abstract

Rapid growth in the global population requires expansion of building

stock, which in turn calls for increased energy demand. This demand

varies in time and also between different buildings, yet, conventional

methods are only able to provide mean energy levels per zone and are

unable to capture this inhomogeneity, which is important to conserve

energy. An additional challenge is that some of the attempts to conserve

energy, through for example lowering of ventilation rates, have been

shown to exacerbate another problem, which is unacceptable indoor air

quality (IAQ). The rise of sensing technology over the past decade has

shown potential to address both these issues simultaneously by providing

high–resolution tempo–spatial data to systematically analyse the energy

demand and its consumption as well as the impacts of measures taken to

control energy consumption on IAQ. However, challenges remain in the

development of affordable services for data analysis, deployment of large–

scale real–time sensing network and responding through Building Energy

Management Systems. This article presents the fundamental drivers

behind the rise of sensing technology for the management of energy and

IAQ in urban built environments, highlights major challenges for their

large–scale deployment and identifies the research gaps that should be

closed by future investigations.

Key words: Energy sensors; Building technology; Energy management;

Non–domestic buildings; Urban environment

1. Introduction

The issues of energy demand and air quality go hand in hand in city environments.

A major proportion (up to 40%) of the total global energy demand is consumed by urban

buildings [1]; of which, up to ~40% is consumed by commercial buildings [2]. Given that

many nations are actively pursuing carbon reduction plans, and thus energy efficiency,

improved energy management is a key priority. Clearly, the commercial built

environment is a major consume of energy, but this also offers an opportunity to save

energy through advanced management [3]. Yet, saving energy by, for example, reducing

power assisted ventilation results in build–up of pollutants generated indoors by

internal sources, including its occupants. However, replacing indoor with outdoor air in

fact can create a problem for indoor air quality (IAQ) if the air outdoors is polluted,

which is often the case in many urban environments [4, 5]. This is because vehicular

emissions pollute outdoor air and its infiltration leads to deterioration of IAQ [6, 7].

Further complexity is added by the changing climatic conditions and the human

expectations of comfortable indoor environments; both of which increase building energy

requirements for heating, cooling, lighting, and the use of other electrical equipment [3].


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Taken together, all these aspects elicit a need to understand the patterns of energy

consumption, both spatially and temporally, in the urban built environments, and

optimise in a manner which would not compromise IAQ.

Optimal energy use is the key for sustainable building operation and hence there is a

need for the correct combination of energy–efficient building designs, energy saving

technologies, informed behavioural choices, and optimisation based on local climatic

conditions that can lead to substantial reductions in energy consumption [3]. Whilst

newly constructed commercial buildings can offer considerable energy efficiency

improvements, about 60% of the buildings that will be standing in 2050 have already

been built. Previous research has attempted to address the issues of energy

management, but adequate answers to many of the relevant questions are still

unavailable. These are elaborated upon below.

Indoor air constitutes an environment that is particularly rich in different types of

pollutants, including gases such as carbon monoxide (CO), carbon dioxide (CO2), sulphur

dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM) of various sizes, and an

array of organic compounds which can be in both particle and gaseous phase [8, 9]. The

pollutants originate from indoor sources and penetrate from outdoors. Under the

protective enclosure of the building envelope, pollutants mix and interact or grow (when

considering microbes). Some of the pollutants, such as those products originating from

combustion can be sourced simultaneously from both indoors and outdoors.

In most commercial situations, occupants have little opportunity to directly influence

their working environment. A move toward more intelligent and autonomous control

systems may enable buildings, when in use, to better respond to occupants’ needs by

adopting new technologies that enable people to be ‘connected’ to the system, which

through smart control approaches learn from users’ preferences. Some of the key

requirements to address the above challenges are the availability of required data

(including energy consumption patterns, human occupancy and environmental

conditions), both in real–time and long–term, for different building types in order to

develop fundamental understandings of their interrelationships. Today this is possible,

given that both the energy management and air quality monitoring paradigms are

changing rapidly due to advances in the development of portable, low or medium cost

sensors [10] (Table 1). Along with complementary wireless communication

infrastructure, these sensors are capable of reporting highly time resolved data in near–

real time to allow fine–grid mapping of energy demand and consumption patterns [11]

as well as IAQ conditions [12].


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Many modern buildings, such as the Massachusetts Institute of Technology, MIT, Media

Lab, are embedded with such smart sensors and high–tech data management companies

[13] provide software tools to manage, analyse and interpret the data. A network of such

Table 1. Summary of sensor types, their use, features and development history.

Sensor types

Use and features of sensors

Source (development

history)

Temperature Measure the (indoor) temperature [°C].

Average range of values for standard

sensors: –25 to 70 °C.

Average resolution: 0.5 °C.

Doukas et al. [39];

Kolokotsa et al. [45];

Missaouia et al. [117];

Mineno et al. [77];

Kima et al. [118]

Humidity Measure the (indoor) humidity [%].


sensors: 0 to 100 %.

Average resolution: 1%.

Doukas et al. [39];


Kima et al. [118]

Lighting Measure the (indoor) illumination [lx].


sensors: 200 to 1900 lx.

Average resolution: 7.68 lx.

Doukas et al. [39];


Missaouia et al. [117];

Mineno et al. [77];

Kima et al. [118]

Energy consumption Smart meters/Power plug meters. System

for temporal tracking of energy

consumption per item [kWh].


sensors: dependent to the size, type and

use of the building

Doukas et al. [39];

Milenkovic and Amft

[119]; Missaouia et al.

[117]; Martani et al. [53]

Occupancy /

movement

Systems for crowd counting, through WiFi,

GSM or Bluetooth signals, or through

volume recognition with depth sensors

[Number of people].


sensors: dependent on the size, type and

use of the building

Mineno et al. [77];

Das et al. [120];

Mineno et al. [77];

Zhang et al. [121];

Martani et al. [53]

Air quality Measure the concentration of CO2 and

TVOC [ppm] in the indoor environment.


sensors: 0 to 2000 ppm.

Average resolution: 20 ppm.


Kima et al. [118];

Kumar et al.[10];

Snyder et al. [11];

White et al. [122]

Noise level Measure the intensity of a sound [dB] in

the indoor environment.

Average sensitivity: –54 dB.

Stupakov et al.[123]


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sensors enables monitoring of energy consumption patterns together with IAQ

parameters which inform energy management strategies. Furthermore, development

and applications in indoor sensing is evident from a number of recent research articles,

showing their usefulness for a wide range of applications. In particular, recent research

has shown development and application of sensors such as thermostat capable of

responding to grid incentives in residential buildings for saving energy and cost without

sacrificing thermal comfort [14], for surface temperature and heat flow in historical

buildings such as museums [15], carbon dioxide (CO2) sensors for determining

occupancy disturbances in commercial buildings [16], demand-based supervisory

temperature control sensors for measuring temperature at breathing levels in large-

scale rooms [17, 18] or integrated sensing systems for indoor applications [19, 20].

Furthermore, Cartalis [21] presented a review of definitions, challenges and prospects

for resilient cities and pointed out that resilience should not be confined to the ability of

a system to return to its stable state after disruption, but should also include the ability

to adapt and adjust to changing internal or external processes. Related legislation also

plays a crucial role in technology deployment. For example, Kokkaliaris and Maria [22]

reported that the legislative initiatives for smart metering are a precondition to zero

Energy Buildings (ZEB) for the following reasons: (i) because smart metering is expected

to increase consumers’ awareness of the importance of investments in improving the

energy performance of their buildings, and (ii) improved capture of benefits of

distributed micro-generation through smart meters is likely to increase the penetration

of renewable energy sources in electricity generation. Related to ZEB, Salom et al. [23]

analysed grid interaction indicators in net zero-energy buildings with sub-hourly

collected data, and concluded that sub-hourly analysis would give more accurate and

thus useful information. It has been showed that differences between peak values

measured with hourly and sub-hourly time resolution can be significant. Also, if detailed

grid interaction analysis at the individual building levels is required, consideration

should be given to detai sub-hourly analysis [23]. However, a number of questions still

remain: (i) currently is there a need for advanced energy and IAQ management systems

enabled by low–cost sensing, and if yes, why? (ii) Does low–cost sensing have the

potential to alter the traditional way of energy monitoring in the future? (iii) What is the

current state–of–the–art of available energy management and IAQ sensors? (iv) What

are the major challenges in their production and large–scale deployment in the built

environments and associated data processing? Finally, (v) what are the associated

research gaps and where should the future research focus? Targeting these questions

and focusing on the urban built environment (for commercial buildings), this article

presents a comprehensive overview and highlights recent advancements, research

challenges and a way forward for future research.

2. The need


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Global energy consumption for commercial buildings has been increasing over

recent decades, mainly due to increasing populations and economic development

worldwide [2, 24]. For example, the percentage of energy used for transportation,

domestic and service sectors from 1991 to 2011 in the UK has exceeded by ~5% the

energy used by industry [2, 24]. Likewise, in the USA, which accounts for 19% of the

world energy consumption, the amount of energy used by domestic and commercial

building has increased to over 40% of the total in 2009; 70% of which is derived from

fossil fuels [2].

Energy for buildings is used for heating and cooling and also for ventilation and air

filtration. Ventilation is an essential aspect of building operation, and it has been shown

that an increase in ventilation rate can improve occupant health and productivity [25-

30] and reduce the energy consumption of the Heating, Ventilation and Air Conditioning

(HVAC) system of office buildings. This is due to the advantage of free cooling during

mild weather, when the outdoor temperature is equal to, or lower than, the desired

indoor temperature[31]. However, increasing the ventilation rate can also increase indoor

pollutant levels, particularly airborne particles, especially in buildings located in areas

affected by vehicle emissions [32-36] and where urban secondary particle formation

occurs [37]. In such cases, outside air should be filtered before being introduced into the

buildings and hence creating an energy demand. This elevated energy demand of

commercial buildings is heterogeneously spread over the urban landscape, thus placing

uneven demands on the local energy distribution infrastructures. Especially for

electricity, the challenge of the management of this demand against supply is

exacerbated by the growth in the connection of local renewable energy generation

systems (such as roof–mounted solar photovoltaic panels), leading in some cases to

serious issues for electricity distribution network operators controlling power flows to

meet supply requirements whilst maintaining operational efficiency.

Typically, conventional monitoring methods of energy use only provide coarse

measurements over time. Furthermore, such measurements are usually conducted on a

building by building level, with the data often held by building occupants or their

individual supply companies. This offers little ability to model the spatial and temporal

patterns of demand, to identify and target hot spots with peak demands, or to develop

and manage the distribution network more efficiently. Due to the complex nature of the

energy supply system, the financial benefits of energy demand reduction and changes in

the timing of load can accrue to different participants in the supply chain [38], and thus

gathering data across the chain is important.

In recent years, specifically for commercial buildings, interest has grown in more

efficient energy management systems that are capable of integrating the following


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requirements in an optimal way: (i) the guarantee of desirable levels of environmental

quality in all spaces of a building, and (ii) the necessity for energy savings [39]. Even

though individual appliances might be ultra–low power consuming, the energy demand

in buildings is expected to grow as the number of these appliances increase. For this

reason an improvement of the end–use efficiency in non–domestic buildings is urgently

required. For addressing the challenges related to the current and predicted energy

demand of buildings, the research community has identified a few high–priority

objectives such as alleviation of peak loads, minimisation of grid losses, improvement of

the energy efficiency of buildings and loads, and the reduction of the uncertainty about

energy produced by renewable sources [40]. Sensor networks can allow very fine–scale

monitoring of energy parameters, which vary spatially and temporally in built

environments. This is important for both, understanding the energy consumption

patterns as well as energy optimisation in commercial buildings. Sensor networks can

become even more critical in the future as energy sources change. One of the emerging

and promising energy technologies is that of hydrogen (H2) [41]. Therefore, it is

anticipated that these sensors are likely to play a very important role in parts of the

energy sector in the near future.

Implementing strategies such as those described above would require a smart energy

system that (a) is instrumented with a wide range of sensors – from monitoring energy

consumption itself to sensing environmental conditions (temperature, humidity, noise,

air pollution, human occupancy), and (b) uses analytics and automation to achieve the

aforementioned objectives [40]. A Smart Energy System (SES) requires solutions for a

variety of areas including consumer electronic device control, energy management

efficiency, building (particularly commercial) automation, and industrial plant

management [42]. A well consolidated type of SES for buildings is the Building Energy

Management System (BEMS), sometimes known as the House Energy Management

System (HEMS) when applied to residential buildings. The BEMS uses methods to

coordinate the activities of energy providers and consumers in order to best match

energy production capabilities to meet consumer needs, minimise cost and synchronise

electricity demand with the grid [43]. The BEMS is generally applied to the control of

active systems (i.e. HVAC systems), whilst also determining their operating times [39]

through advanced control techniques based on real-time sensing and artificial

intelligence such as neural networks, fuzzy logic and genetic algorithms [44, 45]. With

reference to the use of BEMS for the control of operating time of utilities, major

opportunities could come from the integration of building–scale electricity and heat

generation systems (such as solar photovoltaic and solar thermal on the roof, and geo–

thermal systems). Indeed with the current emphasis on renewable energy sources, one

potential benefit of BEMS is in achieving an optimal integration of the scheduled use of


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energy–consuming equipment (e.g. refrigeration, washing machines) and the more

difficult to predict energy availability from local renewable sources [46].

Therefore the overall vision is to widen the use of BEMS to improve the efficiency of

individual appliances through automatic and responsive control, based on the use of the

locally monitored data (such as dishwashers that operate at times of low power load on

the grid). In this way, there is a potential to create a component of a BEMS based on an

Internet of Things (IoT). Indeed, the vision of IoT is based on the connection of physical

objects by way of communication channels to the Internet, making it possible to access

remote sensor data and to control the physical world from a distance [47]. In this sense a

BEMS-based automatically manages utilities (equipment and services within a

building), based on real–time sensing of both energy source availability and

environmental conditions. This is an important step towards an IoT approach for

creating smart buildings. The majority of recent developments in BEMS have followed

the advances made in computer technology, telecommunications and information

technology [48], and hence capable of achieving energy optimisation by guaranteeing a

satisfactory performance level with reduced energy consumption.

3. The state–of–the–art

In line with the needs for SES, the development of end–user interaction tools, as

well as the analysis of the energy efficiency of the built environment has received

considerable attention in recent years [49-51]. In particular, in the UK research into

smart energy systems is a key industry and government priority, comprising a

significant part of the estimated investment of £200 billion needed in energy

infrastructure over the next decade [52].

Various approaches have been taken to address the efficiency of the system and gain an

understanding of the relationships between human occupancy and energy consumption

[53]. These approaches start from predictive modelling of energy consumption based on

usage profiles, climate data and building characteristics [44], to the widespread

diffusion of smart electricity meters, impact of public information displays and

campaigns serving to modify individual’s behaviour [44, 45], and the use of WiFi

connections [53]. For example, it has been demonstrated that the combination of

economiser cycles (i.e. free cooling from outdoor air) and outdoor air filtration can

simultaneously save building energy and improve indoor air quality within buildings

[54-56]. Numerous other studies of the indoor environment have sought to optimise

indoor thermal comfort and energy consumption [e.g. 57, 58-61]. However, it should be

noted that only a few studies have investigated the impacts on indoor air quality, and

those that did, used CO2 concentration as the sole indicator [62-69]. In addition,

techniques for the optimal feedback control of specific HVAC equipment [3], such as


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pole–placement, optimal regulator and adaptive control [70, 71], have been presented in

the past, as using supervisory methods to determine optimal set points for systems of

equipment, such as genetic algorithms [72] and neural networks [73].

A recently published modeling study [74] developed a multi–component model that can

be used to maximise indoor environmental quality inside mechanically ventilated office

buildings whilst minimising energy usage. The integrated model, which was developed

and validated from the field data, was then employed to assess the potential

improvement of indoor air quality and energy savings under different ventilation

conditions in typical air–conditioned office buildings in the subtropical city of Brisbane,

Australia. The study showed that the application of such a model to the operation of

HVAC systems can significantly improve IAQ and conserve energy in air–conditioned

office buildings that are strongly influenced by outdoor air pollution. Also, in order to

optimise the outdoor air ventilation of a building‘s HVAC system, the model needs to be

integrated into the HVAC control and/or BEMS software. At the same time, a number of

sensors or remote connections are required to connect to the HVAC controls and/or

BEMS to provide input data to run the optimal model. As a result, the output data of the

model could be used to control the position of outdoor air intake(s) to achieve the outdoor

airflow rates required to also deliver optimal indoor environmental quality and energy

usage.

Among the equipment for end–user interaction and “smart regulation” that to date have

received the most success and widespread distribution are the smart thermostats, which

are regulators of thermal–energy equipment (heating or cooling). Smart thermostats are

not necessarily ‘smart meters’ since this term often refers to electricity consumption

equipment that can record energy use over short periods during which prices may be

different. Potentially a smart meter also can send meter readings directly to the supply

company, and often have an in–home display of electricity or gas use [68, 69].

Several examples of relatively low price and powerful smart thermostats or heating

controllers have been developed in recent years. One of the most prominent examples in

this sense is Google Nest, a smart thermostat that gathers data about HVAC usage and

home occupancy; transmits it wirelessly to a central location and optimizes thermostat

settings and schedules. But this is not the only one, as many competitors are recently

proposing alternative solutions. These include KGS Buildings (www.kgsbuildings.com) –

an MIT spin–off that accesses BEMS data, stores it in the cloud, and analyses it to

detect operating faults. Other similar services are offered by Honeywell [75] and

Analytika [76].

Besides smart heating controllers, other non–conventional energy management systems

have been proposed in recent years that control energy consumption using the


http://www.kgsbuildings.com/

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convergence of heterogeneous sensor/actuator networks, such as power–line

communications (PLC), Wi–Fi networks, ZigBee, and future sensor networks. These

systems are generally known as adaptive BEMS [77]. Many examples of adaptive BEMS

exist where wireless sensors and actuator networks (WSAN) have been providing

solutions for effective energy management within buildings. Some of these include

networked appliances with monitor and control capabilities and home networks without

new wiring [78]; residential gateway controllers with plug–and–play mechanisms

integrated with the latest Internet technologies [79]; networked remote meter–reading

systems (that employ distributed structures consisting of meters, sensors, intelligent

terminals, management centre and wireless communication network) based on

Bluetooth wireless communication technology and the Global System for Mobile (GSM)

that is a cell phone communications technology used worldwide in over 80% of cell

phones [80]; decision support models using rule sets based on typical building energy

management systems [39]; and semi–centralised decision making methodologies using

multi–agent systems for BEMS [81]. All the above–noted approaches are based on a

pervasive array of sensors. Most common sensing devices for BEMS are those for

collecting data on temperature, relative humidity, lighting, air quality (most commonly

CO2, but recently also particulate matter), energy consumption (smart meters) and

occupancy (system for counting people). Indeed, this data provides information that can

be used to optimise energy consumption to meet actual needs.

4. Will low–cost pervasive sensing change the future way of monitoring?

Both the energy performance and load management in buildings are key issues to

achieve the “EU Climate and Energy” objectives, namely a reduction of 20% of the 1990

greenhouse gases (GHG) emissions by 2020 and a 20% energy savings by 2020 [82].

BEMS may influence geographically and temporally modify the domestic energy use

(electricity and heat) and therefore can have an impact on the electricity consumption

[85, 86]. BEMSs rely on the deployment of an adequate array of sensors and controls

that continuously monitor the conditions of the environment and adjust according to the

relevant operating parameters (e.g. HVAC, lighting) to guarantee an adequate level of

comfort with the lowest energy consumption possible. In order to provide real-time

response to changing conditions, a BEMS requires three components: building

automation and control, energy efficient technologies and systems and demand

responsive capabilities [83].

The high cost of instruments to date has been a key obstacle to the proliferation of

BEMSs. Nevertheless in recent years, the increasing number of low–cost sensors

available on the market [84] and new methods for sensing have reduced these barriers

by using available by–product of legacy system monitoring, such as WiFi connections as

a proxy for human occupancy [53]. Moreover, the development of new sensors –


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especially those based on novel nanomaterials – has brought affordable products that

are more sensitive compared to state–of–the–art analytical instruments into the market,

resulting in an increase in the quality of data gathered.

A number of devices (both fixed and portable) already available in the market sense the

environmental conditions and energy consumption in real–time at a resolution never

available previously, and trends indicate that the deployment of these devices is set to

grow. In terms of fixed devices, implementation of “smart meters” to update the

conventional electricity and gas metering in buildings, is expected to increase

significantly in the UK because the British Department of Energy and Climate Change

(DECC) aims to have smart meters in all homes and small businesses by 2020 [85]. In

order to reach that goal, over 53 million gas and electricity meters will be replaced [86].

However, particularly relevant to new BEMS that do not deploy static sensors but

instead adopt participatory or people–centric sensing [87-89] employ portable devices to

obtain, share, and use data that are intricately enmeshed with human flows and

activities. Portable devices are becoming increasingly popular, and one of the most

promising among all is the smart phone. The number of smart phone users has rapidly

increased over the recent decade [90] and forecasts suggest that smart–phone

penetration (owning at least one mobile or conventional phone used at least once per

month) worldwide is expected to reach nearly 70% of the population by 2017 compared

with ~58% in 2012 [91].

Ubiquitous mobile devices can capture, communicate, and visualise various kinds of

information. For instance, recent mobile phones are equipped with a wide range of

sensors – including accelerometers, microphones, and Global Positioning Systems

(GPSs) – providing an unprecedented opportunity to design a novel humans–in–the–

loop [89] monitoring and computational environment [92]. These sensors provide

localisation and movement data along with some environmental information and

therefore can be linked to other static monitoring networks originating from equipment

located in buildings (e.g. energy consumption), for analysing the complex inter–

relationships between energy use and people movement [53]. The cost of this kind of

monitoring can be considered affordable for a significant proportion of the public in

developed countries. Also, already available in the market at a cost of less than £200

each [93] are the new generation of smart controls (i.e. Google Nest) that monitor

building energy consumption for an individual room or a set of rooms, depending on the

choice of installation. Noise sensors, such as microphone, mini–noise cancelling units

can be bought for £15–20 each [94], air pollution sensors can be purchased for less than

£100 and humidity and temperature monitors for less than £10 [95]. The integration of

fixed and portable sensors with a control system can offer users context–aware services

through the analysis of data into information concerning the monitored


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microenvironment. In relation to outdoor environments, a specific application is in

management of traffic to alleviate congestion related pollution the Newcastle University

Integrated Database and Assessment Platform: NUIDAP [96-98]. This identifies the

location of pollution problems spatially and temporally in a network and provides

information to address these problems and evidence to allow appropriate actions and

policies to deliver government objectives.

The accuracy of low-cost sensors should be considered on a technology-specific basis by

carefully evaluating manufacturers’ stated performance specifications and installation

instructions. Irrespective of how accurately sensor calibration is performed, they are

sensitive to room specific ventilation conditions and need time to acclimatise when the

monitored environment is changed [6, 11]. Despite concerns over the reliability of the

data captured by the sensors, recent validation studies have reported encouraging

results on their performance. Mead et al. [12] demonstrated good performance of CO,

NO and NO2 sensors that can provide parts-per-billion (μg m−3) level mixing ratio

sensitivity with low noise and high linearity. Likewise, Sekhar et al. [13] tested and

validated performance of the electrochemical prototype of a hydrogen sensor and found

exceptional low-level sensitivity (0.005–2%) and high signal-to-noise ratio. Further,

Leidinger et al. [14] tested the VOC sensor systems based on dynamically operated

metal oxide semiconductor (MOS) gas sensors and found them suitable for applications

down to ppb level even against a large background of ethanol. Likewise, low-cost and

accurate temperature sensors are in ubiquitous use, although fit-for-purpose results

require attention to their placement (near occupied parts of a room, not subject to direct

solar radiation, etc.). High-performance air quality sensors can be affordable if centrally

located in buildings and linked to individual zones via a network of sampling tubes

(http://www.aircuity.com/), which reduces the per-point sensor cost. Furthermore, the

work of Singhvi et al. [15] demonstrated reliability of mobile wireless sensor networks to

optimize the trade-off between fulfilling different occupants’ light preferences and

minimizing energy consumption. With focus on reducing energy consumption, Klein et

al. [16] implemented a multi-agent comfort and energy system (MACES) to model

alternative management and control of building systems. The model, tested using a real-

world building data (including actual thermal zones and temperatures), managed to

reduce energy consumption by 12% and achieved a 5% improvement in occupant

comfortcompared with the baseline control, showing that the sensing technology was

more than adequate for the desired application. Calibration of energy models also

requires good sensing reliability. In calibrating energy models it is crucial to analyse,

interpret and model complex interactions and uncertainty [17, 18] from continuously

updated field data [4, 19, 20], for example, in dynamic commissioning and monitoring of

buildings [21]. Using cell-phone traffic as a proxy for occupancy, builds on completely


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independent sensors – owners purchase cell phones – but is reliable only to the extent

that the owners use their phones, and not wired communication networks.

Low–cost and smart devices with multiple in–built sensors, networked through wireless

links and the Internet and deployed in large numbers, provide unprecedented

opportunities for instrumenting and controlling the new generation of buildings [3].

Nevertheless, despite the availability of low–cost sensors as a key driver of the

emergence of energy management systems, this is not the only necessary factor for a

widespread deployment of real–time BEMS. Another critical element is manipulation,

processing, management and analysis of the data obtained from all these sensors, which

still presents a significant challenge and associated cost.

Also, there is an increasing expectation that advanced power conditioning electronics

will play a role in managing and coordinating power consumption not simply for a

particular load (e.g., a variable speed drive in an air conditioning plant) but also in

response to the dynamic needs and capability of the utility system [109]. This

expectation cannot be satisfied without a consistent and reliable apparatus of data

processing to provide real–time responses as well as accessible services for sensor

network maintenance. Even though a sensor network can now be deployed at a

relatively low cost, the labour to maintain the network and to process the data is likely

to be fairly quickly exceed the cost of the hardware. For this reason, it is only

reasonable to argue that the future of BEMS lies not only in the increase of the type and

sensitivity of low–cost sensors, but also in the availability of accessible and affordable

services for data processing and sensor–network maintenance.

5. The challenges

The challenges ahead for widespread use of BEMS can be summarized as follows:

(i) availability of the sensors; (ii) their cost and quality; and most importantly (iii)

availability of services related to deployment, maintenance and calibration of sensors as

well as data retrieval, analysis and management. Whilst the cost in the first two

categories is decreasing, the running cost remains high which is a barrier to large–scale

deployment. In many cases, one needs to develop BEMS in a generic way so that sensing

system can be tailored to collect the relevant data to inform the actuating system and

meet user needs. Also, there is a need to establish for each case a strong logical link

between the range of variables to measure and actions to be taken in response.

Addressing technical problems related to sensor networks during their operation

remains another issue to be resolved. Sensor networks in general pose considerable

technical problems in data communication and processing, and sensor management;

some of these were identified and researched in the first Distributed Sensor Network


Citation details:



(DSN) program. Because of potentially harsh, uncertain, and dynamic environments in

which sensors have to function, along with energy and bandwidth constraints, wireless

ad hoc networks pose additional technical challenges in network connection, network

control and routing, collaborative information processing, querying, and tasking [110].

The cost of flexible professional services in this aspect can be prohibitive, and in–house

expertise amongst facility managers is generally insufficient to manage sensor

networks. Furthermore, the high cost of intelligent data management means that it is

often dropped during value engineering. There is also an added value in integrating

data for household energy use with that associated with mobility. For example, NUIDAP

[96-98] has been developed to be compliant with the common database of Urban Traffic

Management and Control (UTMC) to initiate traffic management and control. By

adopting generic data processing algorithms and analysis methods NUIDAP could be

extended to embrace input data from energy monitoring systems to interface with

electric power systems operations to provide enhanced control and initiate system

changes. Whilst its outputs would need to be configured to conform to appropriate data

standards used in the energy industry, this can be achieved easily within software

interfaces.

In summary, more progress has been made so far in sensor development for the built

environment (particularly for indoor condition sensing) rather than in management.

Thus, there are an increasing number of sensors ready to be used, but there is yet lack

of low–cost and versatile services for the installation and maintenance of sensors as well

as data analysis. Correct use of the current sensors’ capacity can only be achieved by

developing networks that can collect and process the data across a range of disciplines,

which in turn would allow real–time decision making on the changes required to the

operation of the building or in people’s travel choices or both. It is envisaged that

developing cost–effective solutions for managing large (or moderately large) data sets at

low cost is now one of the main challenges for creating responsive building energy and

IAQ management systems.

6. Conclusions and future directions

The availability of new Information and Communication Technologies (ICT) and

infrastructures are changing the delivery and operation of services in building

management and in many other sectors. There are chances that the recent widespread

deployment of omnipresent ICT infrastructures, the rise of machine–to–machine (M2M)

communication over wired and wireless links, and the new opportunity offered by cloud

computing (which is an emerging technology aimed to provide computing and storage

services over a network [111]), will lead to a new type of BEMS referred to as the

intelligent cloud–based building/home energy management system (CBEMS). Recent

studies [112] have shown cloud computing technologies can help the existing BEMS to


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deal with a large amount of computational and storage resources required to improve

energy management effectively. Overall, trends into the future point to the direction of

Adaptive Energy Management Systems (A–EMS) that are able to provide automated

control of energy consumption by connecting networks of heterogeneous, and

increasingly popular, devices, facilitated by the burgeoning IoT.

Sensors used for outdoor monitoring [10] can be used for indoor monitoring. However

the biggest challenge that remains is detection across a wide range of levels with

accuracy, particularly of the low concentrations, which are expected during the absence

of any indoor sources. Furthermore, current air pollution sensors are incapable of

measuring ultrafine /nanoparticles (<100 nm in diameter) [113] that are potentially

more harmful to public health compared with their larger counterparts [114, 115].

Future research is warranted to develop sensors capable of measuring low

concentrations of gaseous pollutants and the number and size distribution of

nanoparticles. A large deployment of these sensors is likely to add burden to the e–

waste, which is already of great concern [116]. This indicates an urgent need of

assessing carbon footprints over their life cycle with safe disposal strategies after the

end of their use.

Significant steps have already been taken in the direction of A–EMS. In particular, the

decrease in cost of senor technologies is a major achievement (i.e. a sensor deployment

at a house or on–street can be conducted for less than £1000). Nevertheless, along with

the introduction of inexpensive sensors there needs to be a general progress in other

related areas such as behavioural changes in travel choices and in the use of energy and

acknowledgment of the individual needs/preferences of building occupants. Progress is

also needed for affordable processing of collected IAQ and energy data to realise systems

for real–time monitoring to provide knowledge of network status to inform appropriate

response. Research in these areas is likely to grow significantly in the coming years in

order to bridge the research gaps with the evolution of sensor technology, which at the

moment appears to be ahead of its time.

7. Acknowledgements

Except otherwise indicated, the views expressed in this paper are those of the

authors. Authors do not certify, endorse or recommend to any trade names and

commercial products that are referred in this article. Technical advice by Cambridge

CSIC members in general, and Dr Ying Jin and Dr Jize Yan in particular, is gratefully

acknowledged.

8. Notes

Authors declare no competing financial interest.


Citation details:



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