Artificial Intelligence For Energy Conservation In Buildings

8
Artificial intelligence for energy
conservation in buildings
Anastasios I. Dounis
Abstract
The problem of energy conservation in buildings is a multidimensional one. Researchers
from a variety of disciplines have been working on this problem. It remains a
challenging and yet rewarding study. In the past three decades, a plethora of scientific
and technological publications on energy conservation in buildings have been presented
in international journals. In this work, we discuss the potentiality of artificial intelligence
(AI) as a design tool in building an automation system. The application of contemporary
AI techniques creates intelligent buildings with the following main goals: energy
efficiency, comfort, health and productivity in living spaces. Two modern domains of AI
that are widely used in buildings are computational intelligence (CI) or soft computing
and distributed artificial intelligence (DAI). DAI includes intelligent agents (IAs), multi-
agent systems (MASs) and ambient intelligence. However, there is a lack of systematic
review of research efforts and achievements mainly on IA and MAS domains. This
chapter briefly presents expert systems and CI techniques and outlines how they
operate. The major objective of this chapter is to illustrate how IAs and MASs may play
an important role in conserving energy in buildings.
B Keywords – artificial intelligence; building energy management systems; computational intelligence;
energy conservation; fuzzy systems; grey predictor; intelligent agents; multi-agent systems
INTRODUCTION
Theprincipleofenergyconservationisalinguafrancaamongscientistsandengineers(Jeltsema
and Scherpen, 2009). In this paper, we present a review of the contribution of artificial
intelligence (AI) techniques to energy conservation in buildings. In sustainable buildings,
energy conservation plays a pivotal role; as is known, however, energy consumption and
comfort are two important factors that usually affect each other in opposite ways. Therefore,
modern AI techniques’ contribution in buildings is not unilateral (energy conservation) but is
addressed in the framework of designing a building automation system (BAS) with the goal of
meeting the requirements of energy efficiency and occupants’ well-being.
ADVANCES IN BUILDING ENERGY RESEARCH B 2010 B VOLUME 4 B PAGES 267–299
doi:10.3763/aber.2009.0408 B ª 2010 Earthscan B ISSN 1751-2549 (Print), 1756-2201 (Online) B www.earthscan.co.uk/journals/aber

ENERGY
The construction sector accounts for one-eighth of the total economic activity in the
European Union (EU), employing more than 8 million people. The intense activity in
building construction, in conjunction with the need for energy savings and
environmental protection policy, dictates more reasonable design practices for buildings.
The newly released EU Directive ‘Energy Performance of Buildings’ (EPBD) concerns the
use of energy in buildings and urges member nations of the EU to set stricter
regulations regarding the efficient use of energy in buildings. For this reason, one of the
main goals of advanced building energy management systems is to minimize energy
consumption. Energy conservation in buildings is achieved by controlling heating/
cooling, ventilation and illuminance.
BUILDING ENERGY MANAGEMENT SYSTEMS
AI confronts the problem of energy management in buildings from another scientific
viewpoint (Louis et al, 2006; Duangsuwan and Liu, 2008; Fong et al, 2009; Kalogirou, 2009)
that is completely different from the conventional one, that is, the so-called building energy
management system (BEMS) (Levermore, 2000). The classical BEMS is usually applied to
read all the available data to control active systems (e.g. boilers, air-handling units, chillers,
etc.) or passive systems (e.g. passive heating/cooling, natural ventilation, etc.). This
equipment is known as a heating, ventilation and air-conditioning (HVAC) system. At the
lowest control level, the actual control strategies employed in commercial BEMSs are on/
off control, proportional, integral and derivative control and optimal start/stop (Loveday and
Virk, 1992). At the higher level, there is a heuristic supervisory control system.
THE PROBLEM STATEMENT
In general, a high level of occupant well-being (thermal comfort, visual comfort and indoor
air quality) requires a high amount of energy use; therefore, an optimized balance between
well-being and energy saving is the target that one has to pursue for sustainable buildings.
The problem of energy conservation and well-being in buildings is a multifaceted one; the
optimization of the performance of the whole building is computationally intensive and
extremely difficult to implement in real buildings. This problem is caused by the
necessity of having information on the performance and status of all systems and
equipment in a building and by the difficulties of handling HVAC systems that turn on/
off, systems that change operating states, systems with multiple modes of operation,
etc. For example, the HVAC system is a classical multi-input/multi-output and non-linear
time-variable system with disturbances and uncertainties. Thus it is very difficult to
obtain a mathematical model to accurately describe the process in different operating
states and modes. Also, it should be mentioned that there are some other types of
limitations in practice. For example, the user’s activity level and thermal resistance of
clothing, involved in the predictive mean vote (PMV) equation, cannot be measured by
sensors. The cost reduction of the PMV sensor would have a great potential for HVAC
application (Liang and Du, 2005).
268 A. DOUNIS

A FEASIBLE APPROACH
Contemporary AI and the topic areas of intelligent agents (IAs) and multi-agent systems
(MASs) offer a possible solution to the above problem. The IAs and MASs act with a
distributed strategy and, in this way, manage the computational burden successfully.
The existence of standard communication protocols (Ethernet, BACnet, ARCNET,
ModBus, LonWorks, KNX and Internet) enables the interchange of information and
collaboration between the IAs to achieve the main goals, such as energy efficiency,
comfort, health and productivity in living spaces. Such digital environments, which
incorporate AI methods and techniques and perceive the presence of users and adapt to
their needs, are called ambient intelligence (AmI).
The rest of the chapter is organized as follows. In the second section, we present the
classification and the aim of the BAS. In the third section, we give an overview of AI
technologies. The fourth section presents a brief introduction and important applications
for energy conservation in buildings of conventional AI techniques with the main
component being expert systems (ESs). The fifth section gives the CI methodologies
that are applied mainly for an energy consumption predictor and comfort. The sixth
section describes the architecture and the basic algorithms on IAs. In the sixth section,
we develop an IA for energy conservation, which utilizes a 3-D fuzzy comfort set (FCS)
and a grey energy predictor. The eighth section is devoted to the presentation of the
methodology for the development of MASs and related works in them. In the ninth
section we present AmI for a building environment, which is a new perspective of AI. In
the tenth section we conclude the chapter and present future perspectives.
BUILDING AN AUTOMATION SYSTEM
The latest advances in wireless communication, digital electronics and microprocessors
have made sensors smaller, low powered and cheaper to manufacture (Akyildiz et al,
2002). Owing to their attractive characteristics, wireless sensor networks (WSNs) are
now widely applied to many applications, which include environmental monitoring and
control, intelligent buildings, etc. Nowadays, in intelligent buildings, enormous amounts
of data are collected through WSNs. The aim of the BAS is to utilize these data and to
transform them into intelligence:
Initially, the data processing provides information. The second step is the mapping
between information and knowledge. The third step is the intelligence/behaviour
acquisition from the knowledge organization. AI is envisaged to play a vital role in the
above transformation between data and intelligence.
The BAS utilizes communication protocols, HVAC equipment, AI techniques and
conventional classical control. It employs two layers. The first layer, called the
automation level, includes software/programs for a standard communication protocol
(i.e. BACnet, LonWorks, etc.), controllers, BEMSs, embedded agents, etc. The second
layer, called the field layer, includes a hardware boiler, air-handling units, lights, a chiller,
sensors, actuators, meters and WSNs (Figure 8.1).
Artificial intelligence for energy conservation in buildings 269

Specifically, a BEMS constitutes an intervener between a WSN and equipment and
essentially is a software system that includes database and online techniques such as
decision support systems, advanced automation systems and control. These techniques
regulate the set points of controllers and determine control strategies. The role of a
BEMS is to improve the energy management and performance of a building. The basic
rules concerning energy efficiency are the following (Doukas et al, 2007):
l starting/ending optimization for accomplishing possible energy savings
l procedural hierarchy: rules for cooperation between electromechanical components
and passive systems (moving shutters, windows, etc.) of a building
l energy management optimization: rules that propose actions aimed at reducing
consumption peaks.
The present review chapter focuses on the contribution of AI techniques in the
development of an intelligent BEMS with the aim of energy conservation.
AI TECHNOLOGIES: AN OVERVIEW
AI or cybernetics is a branch of computer science. Various researchers define AI in different
ways. The differences in the definition of AI have two dimensions: one is human centrality
and the other is rationality. The aspect that intelligence deals with rational actions is mostly
adopted. In this view, intelligence deals with the approach to problems through the laws of
thinking: in other words, through clear processes of reasoning (Aristotelian reasoning). The
rational approach results in systems that are a combination of mathematics and
technology. Thus, AI involves systems that operate rationally. One definition of AI is:
artificial intelligence is the study of agents that exist in an environment and perceive and
act (Russel and Norving, 1995).
Conventional AI is an attempt to mimic human behaviour by expressing it in symbolic
representation, that is, a structured knowledge base. This symbolic representation provides
FIGURE 8.1 Schematic diagram of classification of building automation
270 A. DOUNIS

a solid basis for modelling human expert knowledge if explicit knowledge is available.
However, knowledge acquisition is a difficult procedure and consequently the application of
AI in ill-defined systems is limited. The shortcomings of conventional AI are compensated
by new trends such as computational intelligence (CI), IAs, AmI, etc.
Knowledge representation is one of the most important areas in AI. The most
successful conventional AI product is the knowledge-based system (KBS) or ES. An ES
can emulate human problem solving by representing the expertise in its knowledge
base. An ES usually consists of three major components: a knowledge base, an
inference engine and a working memory. In addition to these three components, an ES
typically contains other components such as a user interface and an explanation facility.
CI was proposed for the construction of new-generation AI (high machine intelligence
quotient and human-like information processing) for solving non-linear and mathematically
non-modelled systems. In addition, CI can be implemented at low cost. Fuzzy logic, neural
networks (NNs) and evolutionary computation are the core methodologies of CI.
Machine learning has received attention from the AI community from the beginning.
Nowadays machine learning is widely used. Therefore, machine learning methods can
be classified into two categories. The first category includes back-propagation, Bayesian
nets and case-based reasoning. These methods require a lot of manual effort to put data
into a form suitable for learning algorithms. The second category includes support
vector machines (SVMs), boosting and genetic programming. These algorithms require
minimal data preparation and can deal with high-dimensional data, requiring only
labelled training sets or an explicit representation of goal states.
Distributed artificial intelligence (DAI) is a subfield of AI concerned with distributing and
coordinating knowledge and actions in multiple-agent environments (O’Hare and Jennings,
1996). Researchers usually distinguish two main areas of research in DAI: (1) distributed
problem solving and (2) MASs.
In smart buildings, BASs and control networks provide intelligent interface devices so
that the user can interact with the components of each function (Figure 8.2).
ESs IN BUILDINGS
Several ESs or KBSs have been developed and realized in buildings or HVAC systems. Their
main goals are:
l system-state monitoring, that is, the deduction of system state from measured
quantities
l diagnostics, that is, for solar radiation from available measurements and other system
input, such as technician observations
l design, that is, building designer assistance to achieve stated goals.
Application programs exist for the fault diagnoses of several HVAC components, HVAC
component selection for new designs and energy resource management. All KBSs used
for diagnostics comprise two major subsystems: a knowledge base and an inference
engine. The knowledge base consists of a rule set of IF–THEN type, such as the
following (Brothers and Cooney, 1989):

IF symptom is too hot and thermostat set-point is correct
And air-flow is good and air-flow temperature is cold
THEN the general cause is cooling load size is too small (cf. 80).
The rule incorporates parameters (e.g. symptom and thermostat set point) and values (e.g.
too hot). A general cause parameter, set by application of the rule, reflects whether all rule
conditions are met. The ‘cf. 80’ term is an uncertainty factor, reflecting the fact that even if
all rule conditions (knowledge-based vs classical control) for solar-building designs are met,
there is only an 80 per cent certainty that this is actually so. This uncertainty factor is
introduced to allow classic KBSs to handle uncertain situations.
The methodologies constitute the second generation of ESs and have the tendency of
being developed in the direction of, on the one hand, specialized knowledge (expertise
orientation) and, on the other hand, in the development of applications in specialized
problems (problem oriented). The problem of energy conservation in buildings is
included in the methodology of the second category of rule-based systems. One of the
well-known methods of representation of knowledge in the ESs is productive
representation as CLIPS (C Language Integrated Production System). The basic structure
of a rule-based system with forward chaining is described by the following algorithmic
equations:
Rule-based systems ¼ Expert system ½8:2
Expert system ¼ Knowledge base þ Inference ½8:3
Knowledge base ¼ Facts þ Procedural knowledge ½8:4
Procedural knowledge ¼ Linguistic rules ½8:5
FIGURE 8.2 Schematic diagram of classification of AI
272 A. DOUNIS

The creation of linguistic rules and facts constitutes the knowledge base of the ES. The
overall organization of a production system can be explained with the help of Figure 8.3.
The three basic components of a production system are a working memory for data
(facts), a production memory for rules and an inference engine, whose function is to
infer new facts from existing facts and rules, to insert the new facts into the working
memory, and then to continue this procedure of discovering new facts via the rules
through the new store of facts in the working memory until no further facts can be inferred.
RELATED WORK
Doukas et al (2007) developed an intelligent decision support model using rule sets for the
management of the daily energy operations of a building and the guarantee of comfort
level. The decision unit is included in a typical BEMS. The decision support unit uses
two groups of rules. The first group of rules ensures comfort conditions and the second
rule set includes rules concerning energy efficiency. The decision unit was implemented
using the following software tools: MS Access, Visual Basic and CLIPS. The proposed
system was applied in a typical office building in Athens and recorded a significant
energy saving of approximately 10 per cent.
Kaldorf et al (2002) proposed a diagnostic tool (performance audit tool) based on an ES
for the detection and diagnosis of underperformance to assist building operators. Decreased
performance is a deviation from correct operation in terms of energy consumption and
comfort level. The cause may be total or partial component failure, wrong parameter
settings, operator errors, undersized system capacity, changes of building zone usage,
etc. However, the performance audit tool does not receive user input and in many cases
may be unable to indicate the exact cause of a detected underperformance.
Guo et al (1993) developed a prototype software tool integrating knowledge-base and
database approaches to solve lighting-retrofit problems for energy conservation and
management purposes. The architecture of the system is built on the concept of a
knowledge-based ES and its links to a database.
FIGURE 8.3 Structure of a production system (ES)

Clark and Mehta (1997) proposed a methodology to integrate the data within a building
management system (BMS) via a single multi-media networking technology and providing
the BMS with AI through the use of knowledge-based systems technology. By means of AI,
the system is capable of assessing, diagnosing and suggesting the best solution. This
chapter outlines how AI techniques can enhance the control of HVAC systems for
occupant comfort and efficient running costs based on occupancy prediction. Also, load
control and load balancing are investigated. Instead of just using pre-programmed load
priorities, this work has investigated the use of a dynamic system of priorities that are
based on many factors such as area usage, occupancy, time of day and real-time
environmental conditions. This control strategy, which is based on a set of rules running
on the central control system makes use of information gathered from outstations
throughout the building and communicated via the building’s data-bus.
DISCUSSION
The knowledge representation in ESs is pre-defined explicit human expertise (e.g. a building’s
operator) in language forms or symbolic rules. The ES does not incorporate occupants’
preferences in terms of their reaction (feedback) to the particular environment parameters
and thus it is characterized as non-dynamic and without learning ability. ESs are mainly
used as diagnostic and energy management tools in a BEMS (Table 8.1).
COMPUTATIONAL INTELLIGENCE
CI methodologies focus on an attempt to mimic nature in problem solving. CI technologies
consist of several computing paradigms, which are mainly NNs, fuzzy logic systems (FLSs)
and genetic algorithms (GAs). These approaches are synergistic, incorporate human
knowledge, manipulate imprecision and uncertainty effectively, and learn to adapt to
changing building environments for energy saving and comfort level in the living space.
ENERGY PREDICTION METHODS
In the first part of this section, we focus on FLSs, grey systems, SVMs, time series analyses
and GAs for the energy consumption predictor in buildings. A review of applications of NNs
TABLE 8.1 Summary of expert systems
SYSTEM PERFORMANCE/REMARKS REFERENCES
Intelligent design support model Energy saving of approximately 10% Doukas et al
(2007)
Diagnostic tool Diagnosis for energy consumption and comfort level Kaldorf et al
(2002)
Software tool: combination of
knowledge base and database
Solving lighting-retrofit problems for energy conservation Guo et al
(1993)
Networking BMS and
knowledge-based systems
Control of HVAC systems for occupant comfort and efficient
running costs based on occupancy prediction
Clark and
Mehta
(1997)
274 A. DOUNIS

for predicting energy consumption in buildings was presented by Kalogirou and Bojic
(2000) (Figure 8.4).
RELATED WORK
Kubota et al (2000) developed a prediction system based on genetic programming and
fuzzy inference systems. Genetic programming is applied for feature extraction and
selection, and fuzzy inference is used for building energy load prediction. The method is
compared with the Kalman filtering algorithm and a feedforward NN with four layers.
Although the NN is better for load prediction, the proposed method can extract
meaningful information from the measured data and can predict the building energy
load of the next day.
Michalik et al (1997) modelled the energy-using behaviour of residential customers by
the use of a fuzzy logic approach. Fuzzy filters are applied to transfer the uncertainties of
customer declarations expressed in linguistic variables into parameters of structural
models. The application of fuzzy filters provides average patterns of energy consumption
with time averaging. The fuzzy filters differ significantly from the statistical approach
where averaging is carried out across a sample of customers. This method allows the
reduction of sample sizes in surveys, reducing the costs of model development.
Jana and Chattopadhyay (2004) formulated a fuzzy multi-objective energy resource
allocation program with the following three objectives: minimization of the total cost of
direct energy; minimization of the use of non-local sources of energy; and maximization
of overall efficiency, that is, minimization of the total energy use of domestic lighting in
order to serve rural planning objectives.
Karatasou et al (2005) introduced a new approach for the prediction of hourly energy
consumption in buildings. The proposed method uses non-linear chaos time-series
analysis techniques for the reconstruction of energy consumption time-series and the
estimation of dynamic invariants and artificial NNs as a non-linear modelling tool. The
main advantage of the proposed predictor is that it uses only measured energy data
from real buildings.
Ozturk et al (2004) developed energy input estimation equations for the residential–
commercial sector (RCS) to estimate future projections based on the GA notion and
examined the effect of design parameters on the energy input of the sector. For this
purpose, the Turkish RCS is given as an example. The GA Energy Input Estimation
FIGURE 8.4 Schematic diagram of classification of energy load prediction technologies

Model is used to estimate Turkey’s future residential–commercial energy input based on
gross domestic product, population, import, export, house production, cement production
and basic house appliance consumption figures. It may be concluded that the three forms
of the models proposed here can be used as alternative solution and estimation techniques
to available estimation techniques. It is also expected that this study will be helpful in
developing highly applicable and productive planning for energy policies.
Lai et al (2008) proposed the use of SVMs as a data mining tool applied to building
energy consumption data from a measurement campaign. Experiments using an SVM-
based software tool for the prediction of the electrical consumption of a residential
building were performed. The data included one year and three months of daily
recordings of electrical consumption and climate data such as temperature and
humidity. The learning stage was done for the first part of the data and the predictions
were done for the last month. Performances of the model and contributions of
significant factors were also derived. The results show good performances for the
model. The second experiment consisted of model re-estimations of a year’s worth of
daily recorded data set lagged at one-day time intervals in such a way to produce a
temporal series of influencing factor weights along with model performance criteria.
Finally, a perturbation was introduced in one of the influencing variables to detect a
model change. Comparing contributing weights with and without the perturbation, the
sudden contributing weight change could have diagnosed the perturbation. The
important point is the ease of production of many models.
Li et al (2009) introduced four modelling techniques for the prediction of hourly cooling
load in a building. In addition to the traditional back-propagation neural network (BPNN), the
radial basis function neural network (RBFNN), the general regression neural network
(GRNN) and the SVM are considered. All the prediction models were applied to an office
building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is
based on root mean square error and mean relative error. The simulation results
demonstrate that the four discussed models can be effective for a building’s cooling
load prediction. The SVM and GRNN methods can achieve better accuracy and
generalization than the BPNN and RBFNN methods.
FLSs, REINFORCEMENT LEARNING AND GAS FOR ENERGY
CONSUMPTION AND COMFORT
In the second part of this section, we focus on FLSs, reinforcement learning and GAs for
energy consumption and comfort. The need to achieve energy savings and to guarantee
comfort conditions, taking into consideration users’ preferences, drove researchers to
develop intelligent systems to achieve a balance between energy management and
users’ preferences in buildings. These systems are designed to monitor and control the
environmental parameters of a building’s microclimate and to minimize energy
consumption and operational costs.
Kajl et al (1997) proposed a neural-fuzzy assistant, which acts as a decision support
system and helps to perform estimations of office building energy consumption quickly
and easily. The neural-fuzzy assistant presented in this chapter allows the user to
determine the impact of 11 building parameters on the electrical annual and monthly
276 A. DOUNIS

energy consumption, annual and monthly maximum electrical demand, and cooling and
heating annual consumption and demand. The NNs’ training and testing data set and
fuzzy rules used by the system are based on the simulation results of numerous office
buildings. The simulations were carried out with the DOE-2 software program.
Kolokotsa et al (2002) proposed a fuzzy controller for the control of indoor comfort
parameters at the building zone level. The occupants’ preferences are inserted in the
fuzzy controller and GA optimization is applied to properly shift the membership
functions of the fuzzy controller in order to satisfy the occupants’ preferences while
minimizing energy consumption. The energy consumption before and after GA
optimization is analysed and the steady-state error is reduced after GA optimization.
Lam (1993) developed an optimal energy control strategy based on GAs. The power
consumption of the air-conditioner was chosen to be the objective function to be
minimized using GAs. The results showed that the reduction in power consumption was
achieved in most cases while maintaining a high degree of thermal comfort.
Alcala et al (2001, 2003) developed fuzzy logic controllers that control HVAC systems
concerning energy performance and indoor comfort requirements that are tuned and
optimized by GAs. The fitness function characterizes the performance of each tested
controller for thermal comfort, indoor air quality, energy consumption and system stability
criteria. With the tuning process of GAs, energy consumption is gradually decreased so
that an improvement of almost 16 per cent is achieved. If energy consumption continues
to decrease, this happens at the expense of stability. The indoor comfort goals are met.
Packham et al (2008) proposed a system called intelligent control of energy (ICE) that
controls the energy consumption in buildings using hybrid intelligent computing
techniques. A hardware protocol is used that allows ICE to interface with a BMS over the
internet. ICE has an advantage over traditional BMS optimizers because it uses forecast
weather to predict internal conditions and uses intelligent techniques, thus providing more
accurate results. Some of the research issues around data pre-processing and the hybrid
techniques used are also examined. The architecture was initially designed with a
practical application in mind and this chapter shows that intelligent techniques can be
deployed in a commercial situation; the architecture allows further features and an
extension of the ideas to be added when they have been fully tested and researched. The
environmental and monetary drivers in this area provide a platform to push the boundaries
of intelligent computing research. The architecture of a hybrid optimizer uses NNs, GAs
and fuzzy logic to optimize energy usage while predicting future start times or set points.
Dalamagkidis et al (2007) proposed a reinforcement learning controller that is developed
and simulated using the Matlab/Simulink environment. The reinforcement learning signal
used is a function of the thermal comfort of the building occupants, indoor air quality and
energy consumption. This controller is then compared with a traditional on/off controller as
well as a fuzzy-PD controller. The results show that even after a couple of simulated years
of training, the reinforcement learning controller has equivalent or better performance
when compared with the other controllers. The main benefit from the use of reinforcement
learning in a BEMS is that the controller continually learns and improves on its policy.
Specifically, it is possible to create pre-trained controllers with a general knowledge of the
building. These controllers will then gradually adapt to optimize their behaviour with

respect to the specific characteristic of the building/space they are used in. Additionally, this
controller could adapt to changes of the building characteristics stemming, for example, from
equipment ageing or replacement, leaks, etc., which cannot be taken into account with other
controller designs.
Hagras et al (2008) showed how CI could be employed in our living spaces to help such
environments to be more energy efficient, intelligent, adaptive and convenient for users. Our
living environments are gradually being occupied by an abundant number of digital objects
that have networking and computing capabilities. After these devices are plugged into a
network, they initially advertise their presence and capabilities in the form of services so
that they can be discovered and, if desired, exploited by the user or other networked
devices. With the increasing number of these devices attached to the network, the
complexity to configure and control them increases, which may lead to major processing
and communication overload. Hence, the devices are no longer expected to act as just
primitive stand-alone appliances that provide only the facilities and services they are
designed for, but to also offer complex services that emerge from unique combinations of
devices. This creates the necessity for these devices to be equipped with some sort of
intelligence and self-awareness to enable them to be self-configuring and self-
programming. However, with this ‘smart evolution’, the cognitive load to configure and
control such spaces becomes immense. One way to relieve this load is by employing AI
techniques to create an intelligent ‘presence’ where the system will be able to recognize
the users and autonomously programme the environment to be energy efficient and
responsive to the user’s needs and behaviours. These AI mechanisms should be
embedded in the user’s environments and should operate in a non-intrusive manner.
DISCUSSION
Energy consumption prediction is a very important factor in an intelligent BEMS. Predicted
energy data allow us to create suitable policies for energy saving. Miscellaneous advanced
techniques are applied for energy consumption prediction. Generally, these methods give
better results than classical statistical approaches. The aim of a future energy predictor will
be to turn to advantage only measured energy data. Methodologies such as FLSs,
reinforcement learning and GAs are employed to develop intelligent systems with the
aim of optimizing energy efficiency and occupants’ well-being. The mentioned works are
testimony to the potential of these methods (Table 8.2).
IAs: BACKGROUND
In the approach of AI through the laws of thinking, emphasis is given to the correct
derivation of conclusions. Best results are achieved when rational action is applied, and
this can be done by using rational agents. A rational agent acts in a way that is optimal
in regard to either the clarity or ambiguity of the information that it accepts.
Consequently, the use of rational agents is fundamental in the AI approach. A rational
agent that realizes the best possible action in a given situation is an IA. IAs are programs
with knowledge, intelligence and the ability to take actions to change the environment
to achieve some goals gradually.
278 A. DOUNIS

WHAT IS AN AGENT?
There is a variety of definitions of the word ‘agent’. Russel and Norving (1995) defined an
agent as: ‘An agent is anything that can be viewed as perceiving its environment through
sensors and acting upon that environment through effectors.’
Figure 8.5 depicts a generic agent within its environment. The agent receives input
from its environment and, through a reasoning system, produces suitable actions that
react to the environment in order to modify it. An agent may be a controller, a room, a
building, a robot, a teacher, etc.
A SIMPLE AGENT EXAMPLE
IAs are designed and implemented in controls and robotics. In automatic control, a
controller has the characteristics of an IA. The example that follows describes a simple
agent and its behaviour. A simple agent is the thermostat for a heater (effector or
actuator). The thermostat receives input from the temperature sensor (percept), which is
embedded in the environment (room). If the temperature is cold (state 1), then the
thermostat-agent turns on (action 1) the heating; else if the temperature is OK (goal)
TABLE 8.2 Summary of energy load prediction methods
METHOD REFERENCES
Fuzzy logic and GAs Kubota et al (2000); Michalik et al (1997); Jana and Chattopadhyay (2004); Kolokotsa
et al (2002); Ozturk et al (2004); Lam (1993); Alcala et al (2001, 2003)
SVM, BPNN and GRNN Lai et al (2008); Li et al (2009)
Grey theory Dounis and Caraiscos (2007)
Neuro-fuzzy Kajl et al (1997)
Reinforcement learning Dalamagkidis et al (2007)
Non-linear time series
analysis
Karatasou et al (2005)
NNs Kalogirou and Bojic (2000)
NNs, GAs and fuzzy
logic
Packham et al (2008)
FIGURE 8.5 Agent interacts with its environment through sensors and effectors

(state 2), the thermostat-agent turns off (action 2) the heating. Generally, a controller that
maps the inputs into actions means that it can be viewed as a simple agent.
WHAT MAKES AN AGENT INTELLIGENT?
Wooldridge and Jennings (1995) defined an IA as one that is capable of flexible autonomous
action to meet its design objectives. With the characterization ‘flexible’, we mean that the
system must be responsive, proactive and social. The word ‘autonomous’ comes from the
Greek words ‘auto’ (self) and ‘nomos’ (rule or law). A system is autonomous when its
behaviour changes following a fundamental law. For example, biological systems are
autonomous because they operate with mechanisms like self-organization, evolution,
adaptation and learning. Autonomy is a key feature of an agent. The agent contains an
intelligence level ranging from simple pre-defined rules to self-learning AI inference machines.
STRUCTURE OF AN IA
Is an agent a program? Russel and Norving (1995) answered this with the ‘equation’:
agent ¼ architecture þ program ½8:6
The architecture is a computing device, that is, a computer, special-purpose hardware
such as low-level controllers, smart sensors, global positioning systems, processing
camera images, etc. The procedure of making a decision by reasoning with knowledge
is the core to designing the agent program successfully. The agent program is the
mapping from percepts to actions. An important priority in developing an IA is the
description of PAGE. PAGE is the acronym of the words percepts, actions, goals,
environment. PAGE includes basic elements for the selection of agent types.
In a building’s environment, the PAGE description is as follows:
l Percepts: temperature, humidity PMV, indoor air quality, illuminance, etc.
l Actions: auxiliary heating/cooling, valves, open/close windows, etc.
l Goals: comfort, energy conservation
l Environment: building.
From the above PAGE description, the designer selects the agent type, for example,
comfort controller, BEMS, etc.
An agent’s decision-making process is critical in determining which action to take in
order to achieve its goals. The properties of the environment are very important and
have significant implications for the design of controllers and agents. The properties
(Russel and Norving, 1995; Rudowsky, 2004) of a building environment that affect the
complexity of the structure of agent decision-making logic are as follows:
l Accessible vs inaccessible: A building environment is inaccessible because the agents
cannot obtain complete, timely and accurate information about the state of the
environment, that is, on the performance and status of all systems and equipment.
280 A. DOUNIS

l Deterministic vs non-deterministic: There are many sources of uncertainties that arise
from real buildings and user behaviours (Dounis and Caraiscos, 2008). The uncertainties
are derived from changes in the environmental conditions and user behaviours.
Uncertainties are generated by a change in outdoor environmental conditions
(illuminance level, temperature, air velocity, etc.). Uncertainties also arise from the fact
that user behaviours are dynamic and unpredictable. Therefore, the next state of the
building environment is not completely determined by the current state and the actions
of agents. These uncertainties determine that the building is non-deterministic.
l Episodic vs non-episodic: Even slight variations in the events in the building
environment (e.g. present user) change the performance behaviour of the whole
system. Therefore, agent action depends only on the present episode and not on
previous episodes. The building environment is non-episodic.
l Static vs dynamic: Only when the building itself is static do all other parameters change.
A dynamic building environment requires a more complex agent design.
l Discrete vs continuous: In a building environment, an agent can use an unlimited
number of percepts and actions to satisfy its goals. Consequently, the building
environment is discrete.
AGENT PROGRAMS
An agent program is a real program that implements the mapping from percepts to actions.
Russel and Norving (1995) considered four types of agent programs:
l Simple reflex agents: The reflex agent is very simple. A decision-making unit includes
pre-determined condition-action rules. The reflex agent finds the rule whose condition
matches the current situation and then produces the action.
l Agents that keep track of the world: This type of agent program is a reflex agent with an
internal state. The difference is that the current percept is combined with the old
internal state to generate the current state.
l Goal-based agents: Goal-based agents need some sort of goal to work. The
decision-making logic is fundamentally different from the condition-action rules, in that
their rule structure is similar to a controller (e.g. a fuzzy controller). Goal-based agents
are more flexible than previous agents.
l Utility-based agents: There are many control strategies, applied in buildings, that achieve
their goals, but some of them are more reliable or cheaper or have low-quality energy
conservation. Therefore, the goals are not really enough to generate high-quality
performance. If a control strategy achieves high performance compared with another
strategy, then it has higher utility. Utility is a function that maps a state onto a real number
usually into the range [0,1], which represents the ‘goodness’ that describes the associated
degree of satisfaction. Goodness can be the comfort, energy saving, etc.; the task of the
agent is to maximize utility. The utility function can be implemented by a fuzzy system, a
fuzzy set, for example, a type 1 or type 2 or a high-order fuzzy set. The utility function is
embedded and a basic part of the agent program. The structure and properties of a
utility-based agent are appropriate in the development of supervisor systems. In cases

where there are conflicting goals (e.g. comfort and energy consumption), and only some of
which can be achieved (e.g. comfort), the utility function describes the appropriate
trade-off. Also, there is no certainty that the goal of management of multiple sources of
energy in buildings can be achieved by agents. This problem can be solved by a utility
function weighting the importance of the goals for energy conservation.
After introduction of the basic background, the various IA technologies existing today can
be classified (Magedanz et al, 1996). The classification is depicted in Figure 8.6.
The local and networked agents are characterized as single-agent systems. In this
category, the agents are non-cooperative. Usually local agents are called an ‘intelligent
interface’ because the main emphasis is on user/agent interaction. The networked
agents can access not only local but also remote information resources. The MASs, with
cooperation between agents, distinguish between DAI-based agents and mobile agents.
DAI-based MASs coordinate a group of autonomous IAs to solve a complex problem.
Mobile agents are used mainly at large computer networks, for intelligent
communication and management.
INTELLIGENT AGENT FOR RECONCILIATION OF ENERGY
CONSERVATION WITH COMFORT
The energy conservation IA evaluates the energy efficiency and comfort of the building and
changes the controllers’ set points. Intelligent Agent for Reconciliation of Energy with
Comfort (IAREC) is essentially a fuzzy system that has three inputs: occupants’
preferences, predicted energy consumption ^
Eðk þ 1Þ and membership grade of comfort
mcðkÞ. The output of the IAREC is the change in the controllers’ set points (Figure 8.7).
A 3-D FCS
The unit cube geometry of discrete fuzzy sets assists us in defining fuzzy concepts (Kosko,
1996). Comfort is represented as an information granule. In particular, the size of the
information granule of comfort consists of three parts (PMV, ILL and CO2) and the
formal representation of this information granule is a fuzzy set in a fuzzy cube (Dounis
FIGURE 8.6 Schematic diagram of classification of IA technologies
282 A. DOUNIS

and Caraiscos, 2007). Therefore, a 3-D discrete fuzzy set models higher-level uncertainty
than does a type-1 FS. This technique opens up an approachable way to model human
decision making.
The fuzzy a-cut or a-level fuzzy set of A is characterized by:
~
A
a
¼
AðxÞ if AðxÞ a
0 otherwise

or ~
A
a
; fðx; mAðxÞÞjx [ Aa
g ½8:7
Based on the above definition, we can conclude that an a-level fuzzy set is obtained by
reducing part of the fuzziness in the original fuzzy set (Tsoukalas and Uhrig, 1997). In
each iteration, membership grades of the measurements PMV, ILL and CO2 are
computed by the a-level fuzzy set and symbolized as a fit vector ðmPMVa
d
; mCOa
2d
; mILLa
d
Þ.
These grades determine a point in a fuzzy cube.
Let V be a set of three elements, V ¼ fmPMVd
; mCO2d
; mILLd
g. The set elements are the
membership grades of three variables PMV, ILL and CO2, which have been computed
by an a-level fuzzy set. The non-fuzzy power set 2V
contains eight sets. These sets
correspond, respectively, to the eight bit vectors (0, 0, 0), . . ., (1, 1, 1). Empty set Ø lies
at the origin (0, 0, 0) of the cube, and space V lies at the vertex (1,1,1). The 1’s and 0’s
indicate the presence or absence of the ith element in the subset. A fuzzy subset c , V
defines the fuzzy unit (fit) or fit vector:
c ¼ ðmPMVa
d
; mCOa
2d
; mILLa
d
|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
point in a fuzzy cube
Þ [ I3
¼ ½0; 13
½8:8
mc ¼ ½c; c # ½0; 1 ½8:9
c and c denote lower and upper bounds, and mc denotes an interval set, that is, the set of
real numbers from c ¼ a to c ¼ 1.
The fuzzy a-cut set of measured variables PMVðkÞ; ILLðkÞ and CO2ðkÞ defines a 3-D FCS
c with membership function mc. If a ¼ 0.5, then the 3-D fuzzy set is a cube with origin (0.5,
0.5, 0.5) and the optimal comfort value corresponds to the vertex (1, 1, 1). Using the
symmetric fuzzy equality measure (Kosko, 1996), we measure the degree to which fuzzy
FIGURE 8.7 Block diagram of IAREC

set c matches fuzzy set V, that is, the membership grade of a 3-D FCS:
Eðc; VÞ ¼ mcðkÞ ¼ Degreeðc ¼ VÞ ¼
cardinalityðc VÞ
cardinalityðc VÞ
¼
P3
i¼1 minðci ; Vi Þ
P3
i¼1 maxðci ; Vi Þ
½8:10
where k is the discrete time step. The fuzzy equality measure Eðc; VÞ measures the degree
to which fuzzy set c equals fuzzy set V. If c and V are non-empty, then E(c,V) ¼ E(V,c) [
½0; 1; Eðc; cÞ ¼ 1 and Eðc; Þ ¼ 0. The fuzzy equality measure gives a value near 1 if the
two fuzzy sets are almost equal. It gives a value near 0 if they are not equal. The 3-D
FCS is a new representation for the word ‘comfort’. This methodology of approximate
representation of comfort is very significant because it is used in the procedure of
decision making for the IAREC.
A GREY ENERGY PREDICTOR
In recent years, grey models have been successfully employed in many prediction
applications. A grey system is a system that is not completely known, that is, the
knowledge of the system is partially known and partially unknown. The grey system
theory can estimate an unknown system by using only a few data and can characterize
the unknown system by using a first-order differential equation.
The prediction of accumulated consumed energy in one day is a very important
element for the proposed system. The energy is considered as a sequence of discrete
data. Consequently, a first-order grey model and a one-variable so-called GM(1,1) model
are used. The steps of mathematical analysis for GM(1,1) model are (Liu and Lin, 2006)
as follows:
First step: Assume that the original raw data series Eð0Þ
with n samples is expressed as:
Eð0Þ
ðkÞ ¼ ½Eð0Þ
ð1Þ; Eð0Þ
ð2Þ; . . . ; Eð0Þ
ðnÞ; n 4 ½8:11
where superscript (0) represents the original series. In the problem of energy prediction,
the data are positive.
Second step: Pre-processing of original raw data. The original sequence Eð0Þ
ðkÞ is
transformed into a new sequence Eð1Þ
ðkÞ using the first-order accumulated generating
operations (1-AGO). AGO weakens randomness of the raw data to generate a regular
sequence Eð1Þ
ðkÞ:
Eð1Þ
ðkÞ ¼ AGO Eð0Þ
¼
X
k
m¼1
Eð0Þ
ðmÞ; k ¼ 1; 2; . . . ; n ½8:12
Eð1Þ
ðkÞ ¼ ½Eð1Þ
ð1Þ; Eð1Þ
ð2Þ; . . . ; Eð1Þ
ðnÞ ½8:13
284 A. DOUNIS

Third step: We define the grey differential equation (GDE) as:
Eð0Þ
ðkÞ þ agzð1Þ
ðkÞ ¼ ug ½8:14
where zð1Þ
ðkÞ is the sequence obtained by applying the MEAN operation to Eð1Þ
ðkÞ:
zð1Þ
ðkÞ ¼ MEAN Eð1Þ
ðkÞ ¼
1
2
[Eð1Þ
ðkÞ þ Eð1Þ
ðk 1Þ]; k 2 ½8:15
In order to find the solution of the GDE, parameters ag and ug must be solved by means of
the least square error method as:
Eð0Þ
ð2Þ
Eð0Þ
ð3Þ
:
:
:
Eð0Þ
ðnÞ
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
|fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl}
E
¼
zð1Þ
ð2Þ 1
zð1Þ
ð3Þ 1
:
:
:
zð1Þ
ðnÞ 1
3
7
7
7
7
7
7
5
2
6
6
6
6
6
6
4
|fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
B

ag
ug

|fflffl{zfflffl}
Q
; E ¼ B Q ½8:16
^
Q ¼ ðBT
BÞ1
BT
EN
½8:17
Fourth step: When the values ag and ug are derived, substitute them into the solution of the
GM(1,1) model:
^
Eð0Þ
ðk þ 1Þ ¼ Eð0Þ
ð1Þ
ug
ag

eag ðk1Þ
ð1 eag
Þ ½8:18
^
Eðk þ 1Þ ¼ ^
Eð0Þ
ðk þ 1Þ ½8:19
IAREC
The IAREC is an economy behaviour fuzzy system or decision-making machine that is
shown analytically in Figure 8.8. The input and output membership functions (MFs) are
shown in Figure 8.9.
The economy behaviour fuzzy rules of a master agent are the following:
R(1)
: If ^
Eðk þ 1Þ is low and mc is low, then mode is m1
R(2)
: If ^
Eðk þ 1Þ is low and mc is high, then mode is m2
R(3)
: If ^
Eðk þ 1Þ is medium and (mc is low or mc is high), then mode is m3
R(4)
: If ^
Eðk þ 1Þ is high and (mc is low or mc is high), then mode is m4
where:
Mode m1: (ILLd)new ¼ (ILLd)initial, (PMVd)new ¼ (PMVd)initial [8.20]

Mode m2: (ILLd)new ¼ (ILLd)initial
. a1, (PMVd)new ¼ (PMVd)initial +b1 [8.21]
In the modes, the positive sign means cooling and the negative sign means heating. The
factors a1, a2, a3 define the percentage of initial desired illuminance. The factors b1, b2,
b3 define the change of the initial desired PMV. The parameters a1, a2, a3 and b1, b2, b3
can be identified by optimization techniques, for example GAs.
MASs: THE NEXT STEP
The capacity of an IA is limited by knowledge and its computing resources. Hence, IAs
must be able to interact, communicate and coordinate with each other. A multi-agent
system is a set of interactive IAs operating in a distributed environment and working
collectively.
DIVIDE-AND-CONQUER TECHNIQUES FOR COMPLEX SYSTEMS
Techniques that divide a problem into smaller sub-problems, which are consequently
solved, are called divide-and-conquer techniques (Ferber, 1999). They also constitute a
top-down process. In general, there are no standard or classical methods to optimally
FIGURE 8.8 Diagram of IAREC with FCS and grey energy predictor
FIGURE 8.9 Membership functions of input/output IAREC
286 A. DOUNIS

divide a problem into smaller sub-problems. Each complex problem has its own
peculiarities and its analysis may reveal appropriate ways of performing the task.
Therefore, people try to invent heuristic techniques to do the job.
In this case, we solve the sub-problems by designing controller-agents that are
based on fuzzy logic and can be optimized by using GAs. An intelligent supervisor
(Dounis and Caraiscos, 2005) coordinates the operation of the controller-agents. It is an
important procedure because it leads to the normal operation of the entire system. In
other words, it solves the original problem. The design of a MAS consists roughly of
three steps:
l Structuring: Decompose the whole problem into a set of independent partial problems.
l Solving individual sub-problems: Solve the partial problems by designing
controller-agents that know how to solve the partial problems.
l Combining individual solutions: Combine the set of implemented IAs into a coherent
whole by properly coordinating their activities.
WHY USE MASs IN BUILDINGS?
Control engineers often face complicated control problems where they have to design and
implement real-time control systems that use a group of controllers instead of a single
one. In addition, the human factor is involved in the control system, either rewarding or
not rewarding a specific control strategy (reinforcement learning). These systems are
called human centric systems (Pedrycz, 2005). Now, the control engineer has one more
job to do: that of breaking the problem into many simple sub-problems (structuring). The
design of the multi-controller system is performed and the system is implemented on a
more general framework, based on controller-agents. For optimal operation, the
controller-agents are guided by a coordinator-agent (Breemen and Vries, 2001).
In order to control the users’ environment, researchers have followed various
approaches: for example, NNs based on the conventional theory of mechanical learning.
However, these approaches use objective functions that aim to either derive a
minimized control function that satisfies the users’ needs on an average level, or
optimize between a number of conflicting needs (e.g. energy efficiency and users’
comfort). In both cases, users have limited participation in the operation of the system
and, for this reason, they must tolerate some degree of discomfort.
One solution to this problem is offered by combining systems based on behaviour
(behaviour-based systems) with systems based on CI (Brooks, 1997; Callaghan et al,
2000). The main advantage of systems that are based on behaviour is that they reject a
theoretical model and replace it with the real one. The behavioural system is a fuzzy
controller where a GA regulates the knowledge base and the membership functions of
fuzzy sets. The fuzzy controller’s outputs are weighted by the coordinator and then
forwarded to the actuators.
Implementation methods for multi-agent control systems are fuzzy logic, NNs,
neuro-fuzzy systems, Markov chain models, finite state automata, learning automata,
dependencies organization, etc.

RELATED WORK
At the AI lab of the Massachusetts Institute of Technology in the US, Brooks (1997) and his
group worked on an intelligent room project that focused mainly on the user and the
facilities offered to him/her in the room. For this reason, cameras, microphones, etc.
were installed in the building to control voice, monitor faces and gestures, etc. This is a
new research direction in control system buildings.
Coen (1997) proposed, controllers in various zones of the building, as distributed
software agents. Intelligence is distributed to agents and evolves through the
connections and interactions of the agents.
Hagras et al (2003) presented a hierarchical fuzzy system for occupants’ comfort. The
system is adjusted to the occupants’ needs by GAs. The overall algorithm is based on
multi-agent technology. The proposed ‘agent’ is composed of three behaviours (safety,
emergency and economy behaviours) and an adaptable rule set of comfort behaviours
that are adapted according to the occupants’ actual behaviour. An ‘experience bank’ is
introduced that applies rule bases according to an occupant’s behaviour. If the rule base
extracted from the experience bank is not suitable for the user, the GA starts its search
for new consequences for the poorly performing rules. The system interactively learns
the optimized rule base for comfort behaviour in a small set of interactions and
produces similar rules to a rule base learnt by offline supervised techniques. The system
has comparable results to offline approaches (e.g. the Mendel–Wang approach, offline
GA and ANFIS).
Villar et al (2008) developed an energy-saving method for a domestic heating system
based on electrical heaters. Multi-agent system architecture with two fuzzy rule-based
systems has been used: a fuzzy model, to estimate the energy requirements, and a
fuzzy controller, to distribute the energy to all of the installed heaters. The aim is to
reduce the energy spent for heating the house while maintaining the pre-defined
comfort level. The proposal has proved to be valid in realistic simulations, although
some revisions must be carried out prior to integrating it into microcontroller hardware.
The real prototype must also be validated in real situations. This system is to be
included in the local company’s product catalogue.
Hadjiski et al (2007) proposed a new hybrid intelligent system for HVAC control by the
integration of a multi-agent system, dynamic ontology (DO) and ant colony optimization.
The combination of data-driven and knowledge-driven methods results in a significant
improvement of all behavioural indexes of HVAC control systems such as speed,
stability, internal communication rate, robustness and disturbances. The hybrid MAS/DO
system is realized by developing Java-based software according to Foundation for
Intelligent Physical Agents (FIPA) specifications. Simulation results for simplified HVAC
systems are reported to demonstrate quantitatively the effect of hybridization.
Sierra et al (2006) developed an intelligent system architecture that, based on NNs, ESs
and negotiating agents’ technologies, is designed to optimize an intelligent building’s
performance. By understanding a building as a dynamic entity capable of adapting itself
not only to changing environmental conditions but also to an occupant’s living habits,
high standards of comfort and user satisfaction can be achieved. The results are
promising and encourage further research in the field of AI applications in BASs.
288 A. DOUNIS

Davidsson and Boman (2005) proposed a decentralized system consisting of a
collection of software agents that monitor and control an office building. It uses the
existing power lines for communication between the agents and the electrical devices of
the building, such as sensors and actuators for lights and heating. The objectives are
both energy saving and increased customer satisfaction through value-added services.
The results of qualitative simulations and quantitative analysis, based on the
thermodynamic modelling of an office building and its staff using four different
approaches for controlling the building, indicate that significant energy savings can
result from using the agent-based approach. The evaluation also shows that customer
satisfaction can be increased in most situations. The approach presented here makes it
possible to control the trade-off between energy saving and customer satisfaction.
Callaghan et al (2000) proposed how a building can be regarded as a machine and how
the behaviour-based principles first proposed by Brooks for mobile-robot control can be
applied to enable autonomous intelligent-building agents to adapt their control to suit
the occupants. We present a novel approach to the implementation of intelligent
buildings based on a multi-embedded-agent architecture comprising a low-level
behaviour-based reactive layer together with a high-level deliberative layer based on
evidential learning (a case-like learning mechanism). We also present a hierarchical
agent architecture in which mobile agents (residing on body wearable devices) and fixed
agents (residing in buildings) can be integrated, opening up new commercial and
personal possibilities. We discuss how this architecture is being implemented, using a
combination of IP and LonWorks networking technology together with a Java
programming environment. We consider future directions of this work, in particular
how it may play a key role in intelligent interactive environments enabled by emerging
technologies such as mobile phones and embedded-internet devices.
Zeiler et al (2006) reviewed multi-agent intelligent internet-mediated control strategies
and combined the most useful insights into a new technology called Forgiving Agent
Comfort Technology (FACT). Global warming, caused largely by energy consumption,
has become a major problem. During past decades, the introduction of energy-saving
technologies has strongly reduced the energy consumption of buildings. Users’
preferences and behaviour have become central to building services control strategies.
Achieving synergy between end users and buildings is the ultimate in intelligent comfort
control. This new comfort control technology, based on the use of the latest ICT
development in agent technology, can further reduce the energy consumption of buildings.
Qiao et al (2006) developed a multi-agent system for building control (MASBO). Energy
efficiency and occupants’ comfort are two important factors for evaluating the
performance of a modern work environment. While energy efficiency, pivotal to energy
savings, has been improving steadily over past decades, a great effort has been made
to address occupants’ comfort, pivotal to work productivity, too. Not surprisingly, many
researchers have endeavoured to combine the expertise from the two areas to create an
intelligent work environment, where energy efficiency is achieved without compromising
occupants’ comfort. Previous studies provide insightful discussions and exciting
experiments. Most of them, however, stopped short of commercialization and adoption
in daily life due to the limitations of hardware and software technologies at the time.

With the advance of agent technology, WSNs and open standards in building automation/
management systems, it is now feasible to build such an intelligent system for
energy-efficient and occupant-satisfied building control, as envisaged and explored by
those pioneers. The authors introduced some ongoing research into the development of
a multi-agent system that combines an EDA agent model, personalized space, policy
management, building performance quotient, WSNs and building automation/
management systems to provide an intelligent work environment. The MASBO system
acts as a mediator between the BMS and the input system. The input system includes
the WSN and policy management.
Dounis and Caraiscos (2007, 2009) developed an intelligent coordinator of fuzzy
controller-agents (FCAs) for indoor environmental condition control in buildings using a
3-D fuzzy comfort concept as an information granule. The proposed intelligent
coordination model has a hierarchical structure. This centralized coordinator consists of
two subsystems: master and slave agents. These subsystems are implemented by fuzzy
logic rules. The master agent evaluates the energy efficiency of the building and
comfort, and the fuzzy inference mechanism produces signals that activate the slave
agent and change the set points of the controllers. The slave agent is a fuzzy negotiation
machine, which compensates the interaction of the FCAs and manages to avoid
conflicts between them. The FCAs are activated when some conditions determined by
the slave agent are satisfied; otherwise they stay inactive. Finally, the applicability of the
suggested system is demonstrated via a TRNSYS-MATLAB computer simulation. In a
building, the controlled variables are index PMV, illumination level (lux) and CO2
concentration (ppm). The actuators that are being used are the auxiliary heating/cooling
system, mechanical ventilation, shading and electric lighting.
DISCUSSION
Agent technology, thanks to its ability to tackle complex systems, has been successfully
applied in developing intelligent systems for energy efficiency and occupants’
well-being. The MAS is implemented by fuzzy logic; it includes learning mechanisms
and the negotiation process. The collaboration of MAS, WSN and BAS has gained the
attention of researchers in energy management and mainly in developing intelligent
buildings. Sophisticated agent platforms or advanced techniques can be used instead of
embedded agents because these have limited capacity compared with a PC. The
mentioned applications in buildings are testimony to the potential of agent technologies
(Table 8.3).
AMBIENT INTELLIGENCE
AmI is primarily concerned with human–environment interactions. The history of AmI
started in Europe in 2001 with the Fifth European Framework Programme. At that time,
the IST Programme Advisory Group of the European Commission (Directorate General
on Information Society and the Media) introduced the concept of AmI by publishing the
report scenarios for AmI in 2010 (Ducatel et al, 2003). The structure of an AmI system
(Figure 8.10) employs an operational and intelligent layer.
290 A. DOUNIS

The AmI vision describes an environment of potentially thousands of embedded and
mobile devices (or software artefacts) interacting to support user-centred goals and
activity. AmI builds on three recent key technologies: ubiquitous computing, ubiquitous
communication and intelligent user interfaces – some of these concepts are barely a
decade old and this reflects on the focus of current implementations of AmI (more on
this later on). Ubiquitous computing means integration of microprocessors into everyday
objects such as furniture, clothing, white goods, toys, even paint. Ubiquitous
communication enables these objects to communicate with each other and the user by
means of ad hoc and wireless networking. An intelligent user interface enables the
inhabitants of the AmI environment to control and interact with the environment in a
natural (voice, gestures) and personalized way (preferences, context).
In the literature (Brooks, 1997; Coen, 1997; Hagras et al, 2004; Doctor et al, 2005;
Rutishauser et al, 2005; Hagras, 2008), the authors deal with the scenario of ‘ambient
intelligence – AmI’. AmI is a new paradigm in information technology that ‘triggers’
imagination. It is a digital environment that perceives the presence of users and adapts to
TABLE 8.3 Summary of IAs and multi-agent systems
METHODOLOGY PERFORMANCE/REMARKS REFERENCES
IA based on fuzzy logic Intelligent coordinator for balance between
energy consumption and comfort
Dounis and Caraiscos
(2007)
Distributed software agents Application in intelligent rooms and building
zones. Significant energy saving. The personal
preferences not adapted or learned according
to the behaviour of the occupants
Brooks (1997), Coen
(1997), Davidsson and
Boman (2005)
Fuzzy systems, NNs and GAs Learning the optimized rule base for comfort
and energy. ICE
Hagras et al (2003,
2008)
Fuzzy rules Energy saving and well-being Villar et al (2008),
Dounis and Caraiscos
(2008, 2009)
Hybrid intelligent system based on
MAS, dynamic ontology and ant
colony optimization
Significant improvement of all behavioural
indexes of the HVAC system
Hadjiski et al (2007)
NNs, expert systems and
negotiating agents
Optimization of intelligent buildings’
performance
Sierra et al (2006)
Multi-embedded-agent with learning
and LonWorks networking
Intelligent buildings with mobile and fixed
agents
Callaghan et al (2000)
Agent and forgiving technology Integration of user behaviour into the climate
control system and improvement of energy
efficiency of buildings
Zeiler et al (2006)
Collaborative software agents,
learning mechanism and decision
making
MASBO works as an enhancement to an
existing BAS. Dynamic configuration of building
facilities to meet the requirements of building
energy efficiency and preferences of occupants
Qiao et al (2006)

their needs, depending on their behaviour. In such environments, interconnected intelligent
fuzzyagentsareused.Theseagentssupporttheusers’actionsandtheeffectorsofthebuilding.
Figure 8.11 shows how the AmI systems insert in the building environment, interacting
with users and receiving information and effecting automatic action in the building (Ramos
et al, 2008).
RELATED WORK FOR AMI
Hagras et al (2004) developed an AmI environment using embedded agents. The Essex
intelligent dormitory, iDorm, uses embedded agents to create an AmI environment. In a
five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user
behaviour and adapt to user needs. The embedded agent discreetly controls the iDorm
according to user preferences. This work focuses on developing learning and adaptation
techniques for embedded agents. The authors seek to provide online, lifelong,
personalized learning of anticipatory adaptive control to realize the AmI vision in
ubiquitous-computing environments. The authors developed the Essex intelligent
dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.
FIGURE 8.10 Schematic diagram of an ambient intelligent system
FIGURE 8.11 Collaboration between ambient intelligent system, human and building from the perspective of AI
292 A. DOUNIS

Hagras et al (2003) described a new application domain for intelligent autonomous
systems – intelligent buildings (IBs). We present a novel approach to the
implementation of IB agents based on hierarchical fuzzy genetic multi-embedded agent
architecture comprising a low-level behaviour-based reactive layer whose outputs are
coordinated in a fuzzy way according to deliberative plans. The fuzzy rules related to
room resident comfort are learnt and adapted online using our patented fuzzy–genetic
techniques (British patent 99-10539.7). The learnt rule base is updated and adapted via
an iterative machine–user dialogue. This learning starts from the best stored rule set in
the agent memory (experience bank), thereby decreasing the learning time and creating
an IA with memory. The authors discussed the role of learning in building control
systems, and we explain the importance of acquiring information from sensors, rather
than relying on pre-programmed models, to determine user needs. The authors
described how our architecture, consisting of distributed embedded agents, utilizes
sensory information to learn to perform tasks related to user comfort, energy
conservation and safety. In this chapter it is shown how these agents, employing a
behaviour-based approach derived from robotics research, are able to continually learn
and adapt to individuals within a building, while always providing a fast, safe response
to any situation. In addition, it is shown that our system learns similar rules to other
offline supervised methods, but has the additional capability of rapidly learning and
optimizing the learnt rule base. Applications of this system include personal support
(e.g. increasing independence and quality of life for older people), energy efficiency in
commercial buildings or living area control systems for space vehicles and planetary
habitation modules.
Rutishauser et al (2005) described the organization and operation of an intelligent building
controller that consists of multiple agents. The agents communicate with one another by
asynchronous, interest-based, messaging. To facilitate decision making and learning in real
time, each agent only observes and takes decisions about a small part of the environment.
Decisions are taken on the basis of a set of fuzzy rules that represent the knowledge of
the system. There are two groups of rules: static and dynamic. Static rules establish fixed
boundaries for the system, whereas dynamic rules are learned and modified continually.
Our learning algorithm constructs the fuzzy rule base online and unsupervised, from sparse
data that are acquired from the non-stationary environment. The authors described a
multi-agent framework for such IB control that is deployed in a commercial building
equipped with sensors and effectors. The results demonstrate that the framework and the
learning algorithm significantly improve the performance of the building.
Arens et al (2005) described the issues with current building automation technology,
assessed how some applications of wireless sensor technology can increase the quality
of control and improve energy efficiency, and suggested opportunities for future
development. The technology will make the following changes in the near future: to
include building occupants in control loops via information and distributed interfaces, to
achieve demand-responsive electricity management in residential buildings, and to
integrate now-separate building mechanical, electrical, security and fire/safety systems
in commercial buildings. Challenges for researchers and design practitioners are to
develop exploitable applications.

DISCUSSION
The achievement of AmI largely depends on wireless network technology and the
intelligence software used for decision making. Fuzzy learning and the adaptation
technique can be embedded in agents. Also in an AmI environment, the MAS can be
equipped with an unsupervised online learning algorithm that produces a fuzzy rule
base. The AmI system can improve energy efficiency and occupants’ preferences
(Table 8.4).
RELATED WORK FOR INTELLIGENT USER INTERFACE
In smart buildings, the building automation systems and control networks (BAC-net)
(Snoonian, 2003) provide user interface devices (thermostat, valves, keypads) so that the
user can interact with the components of each function (heating, cooling, ventilation,
shading, security). The system allows users to set their preferences (desired comfort
conditions, energy management and occupancy schedule).
Kolokotsa et al (2002) used a smart card unit (kiosk), manufactured by the French
company INGENICO, that performs the interface between the system and the user. The
users’ preferences are monitored via the smart card unit. Considering the users’
preferences collected from the smart card unit for a specific time, such as one week, a
statistical analysis is performed evaluating the average users’ preferences corresponding
to the three indoor comfort controlled variables: PMV index, indoor illuminance and CO2
concentration.
Keyson et al (2000) proposed a mixed-initiative user interface that is an intelligent
thermostat that can reduce energy consumption. An embedded statistical model uses
living patterns to infer user intentions.
TABLE 8.4 Summary of ambient intelligence
Hierarchical fuzzy genetic multi-embedded
agent
Ambient intelligent environment (iDorm). The
system controls iDorm according to user
preferences
Hagras et al
(2003, 2004)
Multiple agents, asynchronous
communication, decisions based on fuzzy
rules. Unsupervised online real-time learning
algorithm
Significant improvement in the performance of
an intelligent building. It does not take into
account personal preferences
Rutishauser
et al (2005)
Wireless sensor technology Goals
1. Occupants’ involvement in control loops
2. Highly flexible location of sensors and
actuators
3. Adoption of mixed-mode and other new
types of air-conditioning systems that require
more sensor information to operate efficiently,
etc.
Arens et al
(2005)
294 A. DOUNIS

In practice, however, fully usable user interface systems are undefined and unrealized
(Penner and Steinmetz, 2002) for many reasons. A user interface device is difficult to use in
different buildings. Each building has different equipment, control systems and
requirements. Even in buildings with the same systems, the environment within which
they operate cannot be foreseen.
Penner and Steinmetz (2002) developed a Dynamic Interface Generation for Building
Environments (DIGBE) that dynamically adapts to the user and data environments.
DISCUSSION
Intelligent user interfaces are devices that improve the way in which people interact with
the living environment. They allow the opportunity and possibility for the users to set
their preferences with regard to the components of the automation system. The aim is
to allow the intelligent user interface to be used in different buildings and to incorporate
adaptation mechanisms (Table 8.5).
CONCLUSIONS
In this chapter, we presented a review of AI technologies that have been applied for energy
conservation in buildings. The development of intelligent control systems in the framework
of CI has set the basis to improve the efficiency of control systems in buildings. Application
of AI technologies to buildings results in so-called ‘intelligent buildings’. The architecture of
a multi-agent system for energy efficiency in a building environment was then presented.
Finally, we referred to a new paradigm in information technology, AI, which is a new
approach towards the creation of an intelligent building environment.
Of course, the related works presented here are neither complete nor exhaustive but
only a sample that demonstrates the usefulness of AI techniques. AI methodologies, like
all other methodologies, have relative advantages and disadvantages. In particular, the
agents are not a universal solution or a panacea; there are engineering problems and
situations in a building where conventional software may be more appropriate. There are
no guidelines as to when an IA or a MAS is more or less suitable for the development of
an intelligent system for energy conservation in buildings.
RESEARCH PERSPECTIVES
Future trends and open questions that are more general are given below:
l energy and thermal comfort issues, passive solutions, naturally ventilated and mixed
mode buildings
l balance between thermal comfort and energy usage
l hybrid control theory that can be used to design a supervisory controller. The task of a
supervisory controller involves the optimal control-based set-point policy generation
l type-2 fuzzy sets (Mendel, 2000), order-2 fuzzy sets (Dounis and Caraiscos, 2007) or
Routh sets supporting the development of higher, conceptually composite concepts
for comfort, user preferences and energy

l granular computing (GrC) as a new paradigm of CI in user-centric systems (Pedrycz,
2005). The collection of complex information entities (thermal comfort, visual comfort
and indoor quality) can be considered as an information granule
l the decreasing cost of hardware and improvements in software will make the wireless
sensor-actuator networks very useful in the BAS (Kintner-Meyer and Conant, 2005)
l in an intelligent building, predictive control can be used in the energy management
system in order to develop passive techniques to achieve comfort and energy
conservation
l the use of embedded agents creates constraints on the possible applicable AI
solutions because these have very limited capacity compared with a PC. Sophisticated
agent platforms or advanced techniques can be used instead of embedded agents
(Qiao et al, 2006)
l occupants’ preferences learning in a shared environment
l the conflict between users’ preferences can be solved with a decision-making system
based on a negotiation process so as to find acceptable preferences.
AUTHOR CONTACT DETAILS
Anastasios I. Dounis: Technological Educational Institute of Piraeus, Department of Automation, 250 P. Ralli
and Thivon Str., Egaleo, 122 44 Greece; aidounis@otenet.gr
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User interface devices The system allows users to set their preferences Snoonian (2003)
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