Download Last-generation Applied Artificial Intelligence for Energy

LAST-GENERATION APPLIED ARTIFICIAL INTELLIGENCE
FOR ENERGY MANAGEMENT IN BUILDING AUTOMATION
Yoseba K. Penya,
[email protected]
Institute of Computer Technology,
Vienna University of Technology
Gußhausstraß6 27-29, A1040, Vienna, Austria
Abstract: Artificial intelligence has devised solutions for scheduling problems that
haven't been already applied to building-automation specific issues and could be
beneficial. This paper presents one of these methods, a parallel-genetic algorithm for the
optimisation of a multi-objective demand-side management system, and outlines the
design of the architecture that supports the use of that algorithm. Copyright © 2002 IFAC
Keywords: Energy management systems, genetic algorithms, parallel algorithms,
scheduling algorithms, automation, artificial intelligence.
1. INTRODUCTION
Energy management is not a new application of
building automation; many research works have
investigated different aspects of it before. The use of
last-generation artificial-intelligence
tools
is,
however, still not frequent. This paper presents an ongoing dissertation that combines the application of
one of such tools with the design of an architecture
that may support its work. The nature of the algorithm
that issues the solution is as critical as the selection of
a proper fieldbus system and the design of energy
consumers that can apply such algorithm.
This paper is divided as follows: section 2 gives an
overview on demand-side management, section 3 is
focused on topics of building automation and energy
management, section 4 outlines the genetic
algorithms, section 5 introduces the architecture
designed to support the optimization carried out by
the algorithm of section 6. Finally, section 7 describes
the scenario and section 8 draws some conclusions
and further work.
2. OVERVIEW ON DEMAND-SIDE
MANAGEMENT
Demand-Side Management (DSM) is a group of
techniques that try to control the energy consumption
of a number of devices with principally two aims:
avoiding sudden load peaks (Rollet, 1993) and
scheduling the energy consumption in order to do it in
the cheapest possible time according to the energy
tariff (Swisher et al., 1997). Energy consumers may
participate in a DSM system in two ways: managing
and adapting their behaviour according to the global
DSM-optimum plan or at least informing about their
future consumption schedule (Penya et al., 2003a). In
a distributed DSM environment, all devices broadcast
the prognosis about their energy consumption and
then, they decide the new plan for each one. The
problem is that this process must be done coordinately, since one device could decide unilaterally
to change its behaviour and if other does so, the
system would never reach the optimum. Therefore,
they require an algorithm that searches such optimal
solution and architecture to support it.
DSM can be modelled as a scheduling problem where
the energy is the resource to be shared, the devices
are the resource consumers and the aforementioned
aims are the objectives to be satisfied. More
accurately, it is a multi-objective optimization
problem, since there is more than one goal (three, as
explained in section 6) to be met simultaneously.
3. BUILDING AUTOMATION AND ENERGY
MANAGEMENT
Traditionally, building automation has dealt with
energy management and it has issued a big number of
solutions. For instance, Palensky et al. (1997) present
a number of devices connected by means of a fieldbus
system and achieving a coarse DSM.
3.1 Fieldbus Systems
− OMG Smart Transducer standard (OMG 2003):
Each device class has an unique identifier.
Information and data of the profile are stored in
one server (in the same network or in Internet).
− IEEE 1451 standard (IEEE 1997): Each device
has the information of its profile stored locally.
4. ARTIFICIAL INTELLIGENCE AND
SCHEDULING
Fieldbus systems (or Field Area Networks, FANs) are
networks or bus systems normally consisting of a
high number of lightweight nodes connected by a
small-bandwidth transmission medium. These
drawbacks have maintained artificial intelligence far
from an intensive use in FANs (Palensky, 1999).
Their low cost, however, makes them a popular
solution in building and industrial automation.
Artificial Intelligence researchers have developed
different approaches to cope with the optimization of
the scheduling of a certain resource among a number
of consumers. One of the results is the evolutionary
algorithm family (EA), a group of computational
models originally inspired by Darwin's evolution
theory (Whitley 1994).
3.2 DSM-able Energy Consumers
4.1 Genetic Algorithms
There are two types of DSM-able consumers. In order
to discover any other DSM-able consumer and to
establish a DSM-process, they must be connected to a
common network, usually a fieldbus system. On one
hand, the devices that can participate in a DSM
system are called active. They are able both to issue a
consumption prognosis and also to adopt a new and
DSM-optimal consumption plan. The prognosis
consists usually on a table that represents the task that
are going to need energy, the amount of this energy
and the planned time for the task. Additionally, there
is a list with the alternatives for tasks that may be
postponed or anticipated (actions that give the DSM
process its meaning).
Genetic algorithms (GAs) form a subtype of EAs that
have evolved into stochastic and heuristic-search
methods. Just as each EA, GAs are based on
simplifications of natural evolutionary processes,
such as selection, survival-of-the-fittest, mating,
mutation and extinction. A standard GA works as
follows (Abramson et al., 1992): After formulating
the problem and modelling it as a group of solutions
or genomes (that constitute the search space or
population), a GA looks for the fittest one.
On the other hand, informative consumers are not
able to adapt their consumption to an optimal plan but
they still can issue a prognosis so that active devices
may take them into account. Even non-DSM-able
devices may be DSM-ificated, as introduced in Penya
et al., (2003a) and therefore be included in the DSM
calculations.
3.3 Profiling
Profiling avoids one of the biggest problems
stemming from plug-and-participate systems:
recognising another entity as community (or system)
member (Penya et al. 2003b) and interacting with it in
a proper way. After classifying its interlocutor, the
device can decide what actions may be done, what
information may be exchanged, etc. Having a number
of well-known profiles, each device can declare itself
as an instance of a certain profile, facilitating typical
ad-hoc networks’ mechanisms such as join and
discovery, and more important, the interaction
between devices (in this case, DSM-able devices).
The profiling of energy consumers is not standardised
yet. It seems, however, that the trends will follow the
footsteps of transducers’ profiling, where two models
may be distinguished nowadays (Elmenreich 2003):
The model must consider the constraints of the
system, which principally are:
− Hard constraints: They cannot physically be
violated, including events that must not overlap in
time. For instance, if scheduling John Doe’s
agenda, John won’t be able to be in two places at
the same time.
− Soft constraints: Preferences that, thought
possible, imply some kind of penalisation. For
example, that John Doe has a meeting with his boss
on Sunday morning, which is something possible
but not preferable because it is during the weekend.
The fitness function (also known as “cost function”)
weights the solutions according to the goal(s),
evaluates penalties of the soft constraints and rules
out the solutions that violate any hard constraint.
Starting from (usually) randomly selected genomes,
they are mated to form a new solution. The mating
process is typically implemented by crossing over
genetic material from the parents to create the genetic
material of the children. This process is shown in
Figure 1.
A
B
C
D
E
F
G
G
them all into consideration when choosing the
possible solutions.
1
2
3
4
5
6
7
8
Parallel Genetic Algorithms (PGAs) are simple GAs
whose operations are carried out in a distributed way.
GAs are especially suited to be parallelized since the
genetic operators (mating, selection, mutation) may
be applied locally after receiving the individuals to
operate with. Thus, they may be executed
simultaneously as long as the population remains
grouped (Whitley 1994).
Parents:
Crossover
A
B
C
D
5
6
7
8
1
2
3
4
E
F
G
G
Offspring:
Figure 1: Uniform crossover in a genetic algorithm
Random mutation is applied periodically to promote
diversity. If the new solutions are better than parents
(the comparison is done using the fitness function),
these are replaced and, if the stopping criteria are still
not satisfied (so, the new solution is not optimal) the
process starts again. Figure 2 illustrates the
procedure.
Initialization, mating (normally crossover) and
mutation operators are representation specific,
whereas selection and replacement are independent.
The representation determines the bounds of the
search space, but the operators determine how the
space can be traversed. Therefore, tailoring the
genetic algorithm is critical to its performance
(Bartschi Wall, 1996).
4.2 A Multi-Objective and Parallel GAs
Multi-objective scheduling problems must satisfy
more than one goal. For instance, having the energy
management as an scheduling problem, as already
introduced, there are many objectives to be fulfilled,
such as: consuming in the cheapest way, as smooth as
possible or as soon as possible. Therefore, algorithms
that optimise these scheduling problems must take
initialize population
selection of genomes
mating of genomes
population
mutation of the offspring
new genomes
fitter than
parents?
Yes
replace
genomes
No
are stopping
criteria
satisfied?
No
Yes
solution found
Figure 2: Flowchart of an standard GA
A more sophisticated approach, called coarse grained
PGA or Island Model (Eichberg et al. 1995), divides
the population into a few demes, where they evolve
separately (usually in different processors). This
alternative is normally used for multi-objective
optimization, since on each island the selection is
made according to different criteria. Migration allows
demes to receive the best individuals of the rest and
this crossbreeding is supposed to mix good features
and enhance the evolution.
Finally, fine-grained PGAs extend the island model to
a grid one, where each subpopulation is connected to
four others and the mating is only possible between
neighbouring individuals. This approach is inspired
by the fact that in the nature, the selection is not
global but local. Compared to the Island Model, finegrained PGAs have an easier way to disseminate good
solutions across the population but it requires a higher
communication effort.
5. ARCHITECTURE
The challenge is to design a plug-and-participate
model where DSM regulations are performed by a
number of DSM-able devices. Note that the system is
distributed per se, since the devices are physically
distributed and so is the knowledge as well (LeLann,
1981).
The system is a network composed of highperformance nodes, as presented in Penya et al.
(2002b): although embedded, enough powerful to
host a lightweight agent platform and a number of
software agents running on it. The nodes are
interconnected by a LONWorks fieldbus system and
each one controls one or more devices. Some of the
nodes might be not directly controlling a device, but
just observing it in order to issue a prognosis and
include the device into the system (the so-called
Virtual Devices (VD), Penya et al., 2003a).
The devices recognise themselves by using an OMGlike profiling. This allows them to include in the
profile not only basic information but also to detail
communication interfaces.
Each node hosts a software agent that represents it in
the DSM system. Either the device itself or the agent
must be able to predict the future behaviour, or at
least to estimate it.
−
Once they have a detailed plan and the possible
alternatives within the next 24 hours, they start the
DSM process in a parallel way. The process finishes
when an optimal solution is found and the DSMactive devices have adopted this optimal plan.
6. ENERGY CONSUMPTION OPTIMISATION
Having depicted here the scheduling of a DSM
system as a multi-objective optimization problem,
there two principal reasons for using a genetic
algorithm:
−
First, their success in this kind of problem has
already been proved, in comparison to other
models. For instance, in Gottlieb (2000), Zitzler
(1999) or Zitzler et al. (2000).
−
Second, GAs can be distributed easily, as
explained in section 4.2. We aim to take advantage
of having many nodes that could perform a simple
task in a coordinated way. Therefore, the nodes of
the network may be used in the execution of the
GA.
Thus, a parallel genetic algorithm has been chosen for
this purpose. PGAs, (and generally, all EAs) bring
implicitly, however, a problem: they are blind to the
constraints. In other words, the algorithm does not
assure that every generation born from feasible
parents will be feasible as well (Palensky, 2000). The
algorithm has been adapted to the problem, so the
fitness function has correctors and is able to evaluate
whether new generations preserve the good
characteristics of their parents, in order to eliminate
them if they don't.
The system is distributed according to the principles
of the Island Model PGA. Nodes (this is, consumers)
that are physically close form one island. For
instance, all the devices from a certain floor or section
in a building. Therefore, an island may also be seen as
a cluster or a sub-network. Moreover, within the
island, each device is considered again to be an
island. This approach enhances the scalability of the
system.
Furthermore, the migration is done only between
neighbouring islands (thus floors) in order to reduce
the network load between islands.
The fitness function evaluates each individual
according the following objectives:
−
As smooth as possible: this objective tries to
avoid consumption peaks by spreading it over the
time.
−
As cheap as possible: the fitness function
calculates the prize of the individual according to
the tariff.
As soon as possible: Each task has a preferred
execution time and some (or none) alternatives.
This objective measures the time from the preferred
to the planned execution time.
7. SCENARIO DESCRIPTION
The system issues a 24-hour prognosis. Thus, the
consumers plan a one-day consumption. A leader
node starts the DSM process and the active and
informative devices broadcast part of their prognosis.
The first generation of individuals formed with this
information are delivered to the islands, so each one
gets a part of the search space to explore. The islands
start the algorithm themselves and spread the
individuals further among their devices. Thus, every
device in the system is occupied carrying out the
genetic algorithms.
There are some parameters of the algorithm that must
be deduced and tuned after the testing of the system,
since it depends too much on the implementation.
First, the number of times that the devices must
broadcast parts of their prognosis in order to form a
new generation of individuals (that are going to be
mated, mutated, etc.). Furthermore, migrations
between islands are done regularly but the frequency
of these migrations is also not clear. Finally, the
number of iterations that the genetic operators are
applied has a very high impact on the network load.
On the one hand, there are 24 hours to search for the
optimal solution; on the other hand, a quick process
may be preferred for whatever reason.
When the iterations planned are finished, the leader
device compares all the found solutions and chooses
the best one. Then, the active elements of the system
adopt the new consumption and wait till the next
DSM process is started.
The scope of these models includes mainly buildings,
with lightning, air conditioned and heating systems
that may be controlled and planned, and factories
with similar appliances.
6. CONCLUSIONS AND FURTHER WORK
This paper presents an on-going dissertation that
models the scheduling of a DSM system as a multiobjective optimization problem. Following the
guideline of many successful experiments, a domainadapted parallel genetic algorithm is used to schedule
the energy consumption of the devices present in the
system. The test-bed is also described, composed of
some DSM-able devices connected to a fieldbus
system and represented by software agents that
synchronously carry out the parallel DSM-solution
search algorithm. This algorithm, an Island-Modelbased PGA allows to present a structure that can be
more easily adapted to the physical situation of the
system (floors, sections, sub-networks). It also
enhances the scalability
Some of the parameter of the system, such as
frequency of the migrations, number of iterations of
the scheduling algorithm and number of population
creating processes, must be obtained empirically,
since they cannot be tuned a priori because they
depend too much on the implementation.
Finally, there are still some interesting topics to
research: as done by Palensky (2000), the comparison
with a non-heuristic search algorithm would be
fruitful and a the tests in real-life conditions (this is,
using real fieldbus systems processors, energy
consumers, etc.) should show whether developing
such an intelligent-but-complicated system is
worthwhile and has practical applications. Similar
researchs have shown that the more complicated is
the coordinated action of a multi-agent system, the
higher is the network load needed (Penya et al.,
2002). Therefore, it seems that the scheduling
algorithm should be chosen regarding its
communication needs as well as its feasibility.
ABBREVIATIONS
DSM
EA
FAN
GA
IEEE
OMG
PGA
Demand Side Management
Evolutionary Algorithm
Field Area Network
Genetic Algorithm
Institute of Electrical and Electronics
Engineers
Object Management Group
Parallel Genetic Algorithm
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