IT applied to Public Lighting Management
How IT Can Improve Public Lighting Management?
Nuno Seca and Fernando Moreira
DICT, Universidade Portucalense, Porto, Portugal
Keywords: Telemenagement, Efficient Management of Public Lighting, PLC, ZigBee, Artificial Intelligence.
Abstract: Efficiency of public lighting management is getting relevant. Due to costs reduction “obligation” some
entities have been falling in the temptation of power off street lighting. Once that this procedure puts in risk
people and goods’ security and considering that there are many lighting flux reduction solutions on the
market it will be proposed a new approach. It was developed a prototype to allow managing of public
lighting in an integrated way and based on Artificial Intelligence. Major goal is to achieve a higher level in
which operational simplification can be made with significant cost reduction and operative optimization.
Besides that, proposed approach, recurring to information systems’ supported on neural networks and
functional layers can be used on this particular domain contributing to benefit public lighting systems in a
remarkable way concerning profitability return of investment and operationality.
1 INTRODUCTION
Public lighting is changing. Public lighting has
always been based on traditional solutions supported
on traditional lamps; however, lighting technology is
going further.
Nowadays we are moving to LED technology
and telemanagement solutions are gaining more and
more relevance. Not so long ago, people’s
expectations were about having as much light as
possible (Box, 2010). This feeling is changing and
populations are getting concerned about energy
consumption and the way to light better (Future,
2012).
We are finally assuming that good light is not
strictly related with too much light. Environmental
conscience is also getting relevance and lamps based
on mercury are now assumed as an intense source of
pollution and are becoming forbidden in several
countries. Another aspect that is getting importance
is the ability to requalify regular lighting
installations with systems to vary lighting
flux(assuming flux as the quantity of light emited by
a lighting source in every directions) in accordance
with several events such as time schedule or
environment behaviors. Also important is that, when
LED is used in public lighting, cost related with
energy consumption can be reduced around 60%
(and we have also to consider that TCO is much
lower when compared with traditional lighting
solutions) (Graves and Ticleanu, 2011).
Even with LED technology (Lenk and Lenk,
2011) and their biggest advantages, flux reduction
can be made to achieve better result concerning
energy cost reduction.
Based on previous considerations future of
public lighting tends to be based on integrated
solutions where protocol variation will be something
regular and not a constraint. More than that and
presuming that current “lighting intelligence” can go
further than scheduled dimming scenarios or sensors
able to make it react due to environment variation,
we’ll see (sooner or later) Artificial Intelligence
assuming relevance on this business, and will
assume relevance and will be revolutionary. Based
on “learning from the past” public lighting will turn
the page and a new chapter will be written.
As in many other areas IT is getting relevance. A
few years ago, something like dynamic flux
variation of public lighting would be interpreted like
something that would never happen. However,
nowadays, energy costs are becoming higher and
higher and also due to global economic crisis, it’s
gaining significant relevance. On one hand, people
are quite concern about ecological footprint and
economical savings on the other hand, they don’t
want to lose their quality of life. We have to find a
way to manage it and telemanagement and flux
513
Seca N. and Moreira F..
IT applied to Public Lighting Management - How IT Can Improve Public Lighting Management?.
DOI: 10.5220/0004554205130518
In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 513-518
ISBN: 978-989-8565-59-4
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
reduction are an option (and a reality) to achieve it.
What is being proposed is to move forward and to
make public lighting more intelligent, cheaper and
easy to manage. This will be a reality. It’s a question
of time.
Due to all of that, this paper propose, it to go
further and to present an advanced solution based on
Artificial Intelligence and functional layers
supported by a middleware platform able to manage
and operate in a single way heterogeneous
telemanagement lighting solutions based on PLC
(CENELEC) and ZigBee (IEEE, 2011).
This paper is presented with the following
approach: First of all, are presented relevant issues
related technologies in use to achieve and implement
telemanagement solutions. Then, we present a brief
summary about ZigBee and PLC technologies.
Following that it’s proposed a solution to optimize
the telemanagent and the way it can be done (based
on heterogeneous technologies integration, Artificial
Intelligence and functional layers). It ends with
conclusion and future perspectives for public light
intelligent management and the way to make it
predictive instead of current approach based on
reactivity.
2 RELEVANT ISSUES
Paradigms related with public lighting are changing
faster and faster and the biggest players on the
market are assuming positions to strike each other.
Most of the biggest companies acting on lighting
business are “dressing to impress” and trying to
move as faster as they can into this new reality.
Some of them have already developed solutions
based on technologies that are not new (except on
lighting business). Some of them have chosen PLC
(Sogexi), others made their option on ZigBee
(Owlet) and many others are still figuring what is
going to be the definitive move of the market to
define their strategy. Future will reveal who made
the best choice and most important than that, who
will achieve better performance on sales and
technology. However this is not the “one dollar
question”. From the economic and environmental
point of view, citizens are hopping the best from
lighting companies but we have to look further and
put in question if lighting reduction based on
predefined schedules is our major role and if it’s the
biggest goal to achieve.
Nowadays we have already solutions based on
PLC or ZigBee to implement telemanagement to
achieve flux reduction and, obviously, to reduce
costs related with public lighting. However, what we
need to have in mind is: Is this it?
3 HOW IT GOES
As said, paradigms related with public lighting are
changing. Currently, if the goal is to implement
telemanagement over public lighting to reduce costs
related, we can find several solutions on the market.
Depending of the type of the luminaries, we can
choose between Tension Reduction, PLC or ZigBee
to achieve significant cost reduction. It’s common
sense that to reduce flux output we may reduce
tension on the luminaries.
In a simple way, if we use 230V to light a lamp
it’s expected to have an “amount” of light.
Following this principle is reasonable that flux
output will be reduced it tension used is also reduced
(keep in mind that flux reduction and tension
reduction does not perform as a linear function).
This is probably the simplest way to achieve our
goal: lighting cost reduction. It seems to be a good
solution, isn’t it? Seems to be simple, fast
commissioning, and cheap. In fact, this is just like
that and it works! But… it works well only if all the
luminaries in each circuit are very similar between
them.
An important aspect that cannot be jeopardized
occurs when tension reduction is made. Some
luminaries will reduce flux more than the others and
it’s almost certain that some luminaries will simply
“switch off” due to tension reduction. (Note also that
due to lamp technology tension reduction may
impact on flickering and variation of lighting color.
Due to this simple explanation, we have to assume
that tension variation is an option to be taken in
consideration but only when luminaries are quite
similar).
Moving into another level of public lighting
telemanagement, we have PLC and ZigBee. Both are
supported by OSI model. PLC is a protocol to
communicate over regular power lines. The network
topology used is bus. This kind of communication is
regulated by CENELEC and it has reduced
frequencies available to be used. The PLC biggest
advantage is that networks infrastructure (in
majority of cases) already exists. However there are
some disadvantages related with it: EMI
(electromagnetic interference), bandwidth, noise and
multipath.
Multipath and noise are quite important aspects
to be taken in consideration once that power lines
were not designed for data communication (M.
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Zimmermann, 2000). However it can be managed
based on spread-spectrum techniques like DSSS
(Direct Sequence), FHSS (Frequency Hopping), TH
(Time Hopping) and MC (Multi-Carrier).
When we talk about ZigBee, we are talking
about a completely different approach
(Organization, 2008). Instead of wires we are on the
wireless universe of capabilities (and taking
advantage of this king of approach – note also that
there are some disadvantages on that). In this case
communication is based on radio frequency and
mesh network is the topology in use. No matter
which technology is used, it’s possible to achieve the
same: energy cost reduction based on dimming, in
accordance with light flux output reduction.
Architectural approach and ZigBee philosophy are
quite different: Once that it uses RF (radio
frequency) to implement communication it’s based
on a more sophisticated concept: instead of a bus
network topology it uses a mesh network which is
much more resilient and fault-tolerant.
Communication speed is much higher (250 kbps)
and aspects like noise or multipath are managed in
an easy and efficient way.
4 HOW IT COULD BE
Once that telemanagement is already a reality and
considering that Information Technology gave a
significant contribution is it reasonable to assume
that it’s done. A shorter perspective would say that
we did it but, in fact, we believe that we are quite far
from what can be done. To have sensors to turn on
lights when movement is detected, (or when sunset
arrives or depending on many others environment
behaviors) is not the future. It might look like but
this is the present (Graves and Ticleanu, 2011).Tests
with it are already being made in some pilot plants.
The first step ahead will be done when real time
integration of heterogeneous technologies occurs. So
far, considering solutions based on PLC (Philipps,
2000); (T.C. Banwell, 2001) or ZigBee (Prasad,
1998); (Ata Elahi, 2009) the network topology
stands on a master controller and “slaves” able to
receive orders, make lighting reductions happen, and
give feedback to the master. First problem to solve is
how to integrate different masters (even when they
share the same technology). Nowadays, two
different luminaires, close to each other but
depending on different masters may assume
different behaviors just because masters are not able
to communicate between them. The second problem
to solve is how to integrate different technologies in
a single telemanagement solution. It’s quite
understandable that any village or city (no matter
how big they are) will not always adopt the same
lighting telemanagement solution. It’s not
reasonable because a single technology is not always
the best solution. So, assuming this, why should a
responsible for the system access several platforms
to do his job? What concerns us is not the amount of
work. What concerns us is how to manage in an
integrated way different systems and systems based
on different technologies.
That’s the reason why we have developed a
prototype for a solution based on a middleware able
to implement the necessary abstraction to allow
these systems to be managed as one. More than that
public lighting must be considered as something to
be intelligent. Based on that presupposition we
propose to manage it taking in consideration that
luminaries can act as a neural network supported by
functional layers able to influence these systems
with a data such as:
Traffic rules;
Holidays’ calendar;
Shopping areas;
Residential and campus;
Waste collection;
Many others aspects that might be
particular and to related people living close
to telemanagement lighting installations.
Based on this kind of approach, it’s possible to
have different kind of behaviors once that in spite of
“intelligence” acquired by luminaries controlled by
the system they will performance in accordance with
environmental assumptions to allow the entire
system much effective. Note that a lighting behavior
in the middle of the night might be quite different
when it happens in a school surrounding area, in a
petrol station or in a residential area.
Table 1 shows relevant weaknesses and aspects
that can be improved (beside parameters shown on
table 1 it was also compared: communication
Table 1: Weaknesses of PLC and ZigBee Lighting
solutions.
PLC ZigBee
Unified control of luminaries?
(considering that exists more
than one master controller)
No No
Allows integration with
protocols used by other
telemanagement technologies?
No
No
Intelligent and adaptive reaction
to environmental changes?
No
No
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technology, signal strength, frequency, signal
modulation, weaknesses, resilience, communication
speed, maximum length between nodes, network
topology and ballast / driver protocol).
Figure 1: Matrix approach for different technology
integration.
Figure 1 illustrates what is being proposed:
Instead of different technological solutions that are
blind to each other (illustrated with different colors),
a logical matrix is created to allow interoperability
for each luminaire. Independently of being managed
by PLC or ZigBee, middleware takes in
consideration what is the luminaire to be acted (for a
sensor, for instance) and what luminaire is supposed
to be acted based on how they are connected to each
other and also based on function layers that supports
Artificial Intelligence (Koch and Segev, 2012) to
operate the entire system. With this new approach,
instead of different data speed of each systems, it’s
possible to manage it based on functional groups
once that middleware only request to each master
(no matter it works on Zigbee or PLC) to act a
specific luminaire. Also note that with this approach
and depending on how neural network (Norvig,
2003) is defined, luminaires can act as a group or
individually. In this approach, neural network to be
defined is quite important. Conceptually, it defines
entrance nodes, nodes to be acted (middle neurons),
and output nodes. (Decision to use neuronal
networks is justified by the way they can handle
advanced data analysis, by self-learning
mechanisms, fault tolerance, capacity of
generalization (even if some data is missing), ability
to ignore “noise” data, adaptability and their major
focus: to be used on real-time applications). Based
on that, it’s possible to define what luminaires must
be acted when an entrance node is activated by
behaviors detected that are supposed to be taken in
consideration. Figure 2 illustrates the principle of
neural networking.
Figure 2: Principle of neural network.
As illustrated on figure 2, entrance neurons
(sensors) are installed on main luminaries (typically
luminaries installed in the beginning of the
functional circuit and usually the first to be acted)
that knows, based on relational information, what
luminaries must be acted every time that a
predetermined event occurs. Middle neurons are
luminaries to acted by influence of a previous
luminaire, and output neurons are luminaries (last in
the circuit) that ends a predetermined path and
closes the event that were triggered by the entrance
neuron.
There is also another aspect to be considered:
fading. Obviously, this kind of system must act in a
smooth way. The way to achieve it is illustrated on
figure 3.
Figure 3: Fading Effect Management.
As illustrated on figure 3, notice that there is an
initial sensor (entrance neuron) that is informed of
luminaries to be acted on a predefined range. The
sensor to act the second group of luminaires is
installed in a luminaire located into the first group
range. When it detects behavior, the second group is
activated before the arrival of the car.
As shown on figure 4, and considering that
systems developed are installed separately (as they
are already installed) and connected to their own
“master system controller”, development to be done
is on the upstream of current systems. As shown,
there is an “Integration Middleware” of different
systems where described approach is implemented
(Artificial Intelligence rules, functional and logical
layers, databases and the protocol translation).
Besides that, and following what is already deployed
by existing solutions, system will be able to be
managed from local networks, Internet, VPN and by
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Figure 4: Architectural Approach.
any type of devices (such as PC, laptops and mobile
devices). The biggest goal, as said, is to provide a
homogeneous platform able to provide single
management that is independent from the “last mile”
solution and able to manage public light in a
predictive and non-static way.
When we think about the difference between
sensors or schedule dimming scenarios when
compared to AI it might be not immediate to
distinguish between them but there is a big
difference: AI makes it happens in a predictive way
while sensors make it happen in a reactive way.
Functionality is a reality on both systems but
operability is completely different. There are many
advantages if AI approach is taken in consideration.
The biggest advantage, probably, is related with the
ability to act in a predictive mode. In fact it forces
the change of current paradigm. Telemanagement is
being used as a brand new buzzword but in fact it’s
often confused with saving of energy consumption
when, in fact, it only means that customer is able to
reach (remotely) lighting systems and operate them.
On current telemanagement lighting systems,
savings on energy costs are achieved based on flux
reduction between certain periods of time. The goal
is: if we have a 100% light output, let’s reduce it
during the hours when streets are (supposed to be)
empty. A problem occurs when someone goes into
the street in the middle of the night. Luminaries
might be lighting to much low. The fastest answer to
solve it is to install sensors able to detect movement
and make luminaries react in accordance. In fact, it
can be a good approach but it will be required to
install as many sensors as many different behaviors
can be taken in consideration. For instance: it will be
required to install one sensor to detect if people
move to left and another to detect if people move to
the right. Once that current telemanagement lighting
systems are reactive and “no-learning” it will never
be able to predict movements and, based on that, it
will never be possible to light only required
luminaries and achieve a more effective cost
reduction. Based on sensors there are no other
approach than light a significant quantity of
luminaries that are “enough” to answer to the more
recurrent behaviours.
Resuming what was said, so far, and concerning
LED technology it’s possible to achieve significant
saves on energy consumption. More than that, based
on flux reduction schedule this value can be
increased with significant impact also on total cost
of ownership and ecological footprint. However,
based on what is proposed public lighting
performance can be optimized if some paradoxes
and traditional approach is shifted. Based on what
was already presented it’s possible to change de
drive from reactive lighting to predictive and more
functional public lighting.
5 CONCLUSION AND FUTURE
PERSPECTIVES
Independently of what has been done and the
evaluation made on technologies like PLC
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(Hrasnica, Haidine, and Lehnert, 2004) or radio
frequency like ZigBee (Walke, 1999), public
lighting can go further and achieve higher levels of
efficiency and usability. In the near future, people
will be aware of it and will be focused on cost
reduction, environmental footprint without
jeopardizing security and public light performance.
Based on AI and principles of neural networks,
(in this particular scenario and based on perception
of previous behaviors, public lighting could always
be reduced to minimum flux and pushed up every
time that people needs light) instead of saving
during part of the night, AI might implement an
“always low” dimming profile and switch to a
higher flux every time that light is really needed and
light just only where light is required. Another very
interesting advantage is that based on AI and neural
networks, lighting groups can be dynamics instead
of static.
There are numerous advantages like significant
and effective cost reduction that will have impact on
maintenance costs and energy consumption
reduction. Other aspect quite important is that
luminaries will last longer once that daily runtime
hour might be reduced. Concerning environmental
care there are many advantages once that CO2
emissions are reduced and luminaries will live
longer and, obviously, won’t need to be replaced so
many times as they are used to.
More than that, the major question is about the
maturity of customer to ask for this kind of solution
and the capability and commercial interest of
lighting players (notice that energy companies –
probably the most relevant players on lighting
business - have significant profits based on energy
selling and they make huge investments that are not
intended to see underexploited) to provide it.
Proposed approach can be considered as a “nice
to have” or something that will never become real
but we have to remember that 5 years ago LED
technology was not an option for public lighting. It
has changed nowadays. It’s common technology and
the commercial differentiation is not significant
because everyone has a “ready to use” solution.
Lighting players’ Differentiation will be done based
on service. In fact, it´s already been done. Currently,
market drivers are changing and public lighting is
becoming to be negotiated based on “lighting point
performance” instead of quantity of supplied
luminaires. Due to it, Public Lighting “intelligence”
might be the most important argument once that it
fits (and extends) most important aspects on public
lighting: Return of Investment, profitability and
operability.
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