Intelligent Buildings Technology - ΤΕΙ...

Intelligent Intelligent Buildings Buildings TechnologyTechnology

Dr D. Kolokotsa

Outline

Introduction to intelligent buildings Introduction to building energy management

and environmental control Simplified calculations Case studies

Building in Chania, Crete Greenhouse ….

Human Population Growth:

Source: United Nations, World Population Prospects, The 1998 Revision; and estimates by the Population Reference Bureau.

207243

285311

349 368404

433481

532583

640

1970 1975 1980 1985 1990 1995 2001 2005 2010 2015 2020 20250

100

200

300

400

500

600

700

IndustrializedEE/FSUDeveloping

History Projections

World Commercial Energy Consumption, 1970-2025

Source: EIA, International Energy Outlook 2003

Energy Growth potential

Energy use will grow strongly, especially among the developing countries (2.8% per year in the developing world).

There will be continued reliance on fossil fuels through 2025; oil remains the dominant fuel type (38% of total world energy use)

Natural gas is the fastest growing source of primary energy (2.8% per year)

Coal will continue to be an important fuel mostly used for electricity generation

Nuclear power will expand in several Asian countries, but most industrialized countries are not expected to undertake new construction of nuclear units

The contribution of renewable energy sources will only be about 8% of total world primary energy use.

World Energy Use by Sector (Total 2001 Use: 426 EJ)

0%

20%

40%

60%

80%

100%

1971 1975 1980 1985 1990 1995 2001

Year

Per

cent

age

of P

rimar

y Fu

el

Industrial Res. Bldg. Com. Bldg. Transport Agriculture

Some promising Building Technologies Superinsulated building envelopes Low-E Electrochromic Glazing on windows: Windows switched

from clear to dark, with voltage, to reduce heat transfer. Smart Buildings: Advanced sensors, energy control (zone

heating) and monitoring systems. Efficient Appliances: Substantial efficiency improvements are

possible reducing energy use by 50% . Thermally activated heat pumps: Use highly efficient heat pump

cycles that are 2 to 3 times more efficient than conventional electric resistance water heaters.

Improved compact fluorescent lamps (CFL) and other lighting advances: One new idea uses sunlight collectors and daylight distribution systems.

Multi-functional equipment and integrated systems: An integrated water heating/space cooling system could be 70% more efficient.

Innovative materials.

IBG (Intelligent Building Group): One that incorporates the best available concepts, materials, systems and technologies integrating these to achieve a building which meets or exceeds the performance requirements of the building stakeholders, which include the owners, managers and users, as well as the local and global community.

Also from IBG but more often quoted: One that maximizes the efficiency of its occupants and allows effective management of resource with minimum life costs

Intelligently designed buildings are those that involve environmentally responsive design taking into account the surroundings and building usage and involving the selection of appropriate building services and control systems to further enhance building operation with a view to the reduction of energy consumption and environmental impact over its lifetime.

Intelligent Buildings Definitions

Intelligent Buildings Definitions IBI (The Intelligent Buildings Institute

in Washington DC, US): one that provides a productive and cost- effective environment through optimization of its four basic components - structure, systems, services and management - and the interrelationships between them.

More….

An Intelligent Building is one that: Provides a productive and cost-effective built

environment through optimization of its four basic components - structure, systems, services and management - and the interrelationships between them…(focused on the benefit of the Owners)

So as to maximize the efficiency of its occupants (focused on the benefit of the Users) ….

And to allow effective management of resource with minimum life costs (focused on the benefit of the Managers) (Environmental and economic impact of creating desired indoor environment)

Building Energy Management Systems aim to optimise the use of energy in buildings by maintaining at the same time the indoor environment under comfort conditions.

Practically, a BEMS is a computerised system that attempts to “control” all or some of the energy consuming operations in a building: HVAC systems (Heating Ventilating and Air

Conditioning) Lighting systems (natural and artificial) Indoor climate

Building Energy Management

BEMS are now available with a wide range of building automation facilities and in many installations BEMS have replaced hardwired controls, with control strategies implemented in software.

BEMS can combine many technologies: Passive heating and cooling Efficient daylight penetration by using suitable shading devices Efficient appliances that reduce the electricity consumption High efficiency windows (e.g. electrochromic). Natural ventilation for indoor air quality and passive cooling. Improvements in building services for HVAC. Building Energy Management and Control.

Building Energy Management Systems-Definitions

Building Energy Management Systems- How much energy can be saved

BEMS Architecture

The structures of BEMS change with evolution of technologies and products.

Early BEMS were centralized energy management systems and first appeared in the 1970s, having been developed in the USA. The central station was based on a minicomputer, which contained the only computing power or "intelligence" in the system, with "dumb" or unintelligent outstations which were boxes or cabinets for relays and connections to sensors and actuators.

Since about 1980, with the rapid development of technologies, the outstations became as powerful as the previous minicomputer, if not more so.

Also, the outstations have gained considerably in processing power giving them "intelligence".

BEMS Architecture

General Architecture

Central Unit

Sensors Actuators

General Architecture

Central Unit

LocalController

LocalController

LocalController

ActuatorsSensors ActuatorsSensors ActuatorsSensors

BEMS Architecture

Four levels

BEMS-Communication

Communication protocols are necessary as communication interfaces between the elements that consist the systems (sensors, actuators, controllers, etc.)

Information should be transferred and delivered in a certain way that is defined by the communication protocol

The compatibility of each element of the BEM system with the communication protocol is an essential parameter when structuring the system

BEMS - Topologies

BEMS Topologies

Network topologies determine the way the Operator Workstation (OWS) is connected with the various equipment: Point-to-Point: The simplest approach where the OWS is directly

connected with an outstation Star: Like Point-to-Point but more than one units are connected to

OWS Bus: The various units communicate independently between them

and the OWS. The extension of the network is simple Ring: Information is transferred around the ring only in one

direction. Each unit recognizes if the information is of its own concern, otherwise information bypasses the unit (token-passing protocol)

Tree or Hierarchical: Units communicate through a tree topology

Sensors

Measurement of solar radiation Temperature sensors Humidity sensors Measurement of wind velocity and direction Flow metering sensors Air pollutants measurement sensors (CO,

CO2 ) Presence/Occupancy sensors

Pyranometer

Sensors

Water TemperatureIndoor Temperature and Humidity

Wind Velocity and Direction

Heat Flow SensorAir Temperature in a ductCO2 SensorPresence/Occupancy Sensor

BEMS-Actuators

The selection of an actuator should be based on 2 main criteria: Control strategy The type of equipment that will be controlled

Reliability: the power of the actuator should respond to real operational conditions (e.g. wind pressure on shading devices)

Time respond: Should be small especially in security systems control or error handling

In case of malfunction same control equipment should be return to a security position

Other criteria: accuracy, compatibility with the network, life time, maintenance, calibration, etc.

Damper ActuatorCeiling FanTriode Valves

BEMS-Actuators

BEMS-Control

Open loop

Closed loop

Sensor Controller Actuator ControlledDevice

ExternalVariable

ControlledVariable

SensorController Actuator ControlledDevice

Set Point

ControlledVariable

+-

On/Off: There are only 2 states of outputs (e.g. in a valve fully opened/fully closed)

BEMS-Control

BEMS-Control

Logic programming: The implementation of the control strategy is based on the use of logical rules:

IF (Room temperature is over 26C) THEN (Close blinds) AND (Switch fans off)

The connected appliances are controlled mainly by On/Off

Strategies

The enthalpy program monitors the temperature and relative humidity or dew-point of the outdoor and return air and then positions the outdoor air and return air dampers to use the air source with the lowest total heat or least enthalpy

The load reset program controls heating and/or cooling to maintain comfort conditions in the building while consuming a minimum amount of energy

The zero- energy band program saves energy by avoiding simultaneous heating and cooling of air delivered to spaces

The occupied-unoccupied lighting control is a time-based program that schedules the on/off time of lights for a building or zone to coincide with the occupancy schedules

BEMS-Control

Fuzzy Logic: This kind of control require the synthesis of a large number of parameters and sometimes is quite difficult to predict the “behavior” of the controller

The use of fuzzy logic can be efficient for controlling complicated parameters such as thermal comfort.

BEMS-ControlThe concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, and

presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership.

This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time.

In this context, FL is a problem-solving control system methodology that can be implemented in hardware, software, or a combination of both.

FL's approach to control problems mimics how a person would make decisions, only much faster.

FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically.

The FL model is empirically-based, using rules i.e. "IF (process is too cool) AND (process is getting colder) THEN (add heat to the process)"

BEMS-Control

Intelligent control – Fuzzy systems The membership function is a graphical

representation of the magnitude of participation of each input. It associates a weighting with each of the inputs that are processed, define functional overlap between inputs, and ultimately determines an output response.

The degree of membership (DOM) is determined by plugging the selected input parameter (error or error-dot) into the horizontal axis and projecting vertically to the upper boundary of the membership function(s).

Triangular mfs is common, but bell, trapezoidal, exponential have been used.

BEMS-Control

BEMS-Control

Intelligent control – Neural Networks Biological systems implement pattern recognition computations via

interconnections of physical cells called neurons. Researchers from such diverse areas as neuroscience, mathematics,

psychology, engineering, and computer science are attempting to relate underlying models for control, the computation that is desired, the potential parallelism that emerges, and the operation of biological neural systems.

The creation and study of intelligent systems by recreating the computational structures of the human (or animal) brain, has fully emerged in only the last two decades.

BEMS-Control

Intelligent control – Neural Networks Three entities characterize an ANN:

The characteristics of individual units or artificial neurons, The network topology, or interconnection of neural units and The strategy for pattern learning or training.

To some extent, the ANN approach is a non-algorithmic, black box strategy, which is trainable.

The purpose is to train the neural black-box to learn the correct response or output (e.g. classification) for each of the training samples.

This strategy is attractive to the system designer, since the required amount of a priori knowledge and detailed knowledge of the internal system operation is minimal.

After training the internal (neural) structure of the artificial implementation it self-organizes to enable extrapolation when faced with new, yet similar, patterns, on the basis of experience with the training set.

BEMS – Control

Neural Networks: The structure of these controllers tries in a certain way to emulate the function of the human brain

They are using mainly in non-linear systems The can divided in various layers. The 1st

layer is composed by the Inputs while the last by the Outputs

A layer may includes nodes that connect this layer with nodes in the next layer through weighted links

BEMS- Control

0 5 1 0 1 5 2 0 2 52 0

2 2

2 4

2 6

2 8

3 0

3 2

ho ur

Am

bien

t Tem

pera

ture

(T)

a c tua l T va luep re d ic te d T

BEMS – Prediction and control

BEMS-Communication protocols

Industrial progress in semiconductor development and growing demands by the end user, e.g. better control performance, have led towards advanced control systems, known as serial networked control network systems. Features of these control systems are: Distributed intelligence, using microcontrollers. Real-time operations are possible. Peer-to-peer architecture. Memory and software programs are provided at node level. Software is implemented in layered protocol stacks.

The limitations of serial networked control systems lie mainly in network expansion, a limited variety of topologies and transmission media. These limitations are overcome by the new generation of distributed control network systems with the following features: Mixing of communications media (twisted pair, power line, radio,

infrared, fibre optics, coaxial). A better, or more complete, implementation of the OSI model with

higher reliability of the (growing) network. Free topology. User-friendly software and available development tools. Connectivity units, gateways, bridges, routers and repeaters.

BEMS- Communication protocols With distributed control network systems a major step towards

intelligent building automation systems has been made, resulting in: Lower operating costs Demands for sharing information Improved human environment, especially work place conditions Improved building performance and economy Similar to a factory plant, a public building includes several types

of network systems, such as: • Building automation systems: responding to external conditions and

controlling the internal environment or generating alarms. • Building management systems: monitoring, managing and storing

control data. • Local area network system: handling information exchange within a

company. • Communication systems: providing links for worldwide communication

and data exchange.

Building automation systems are used for the following automation services and control tasks: Heating Ventilation Air Conditioning (HVAC) Lighting and emergency lighting control Power management Security and protection Transport (lifts)

These automation services are currently supported by communication protocols such as: BACNET ARCNET BitBus CAN EIBUS LonWorks PROFIBUS And many other systems based on RS-232, RS-422, or RS-485

communication standards.

BEMS- Communication protocols

Founded in 1990 by 15 firms, the European Installation Bus (EIB) Association is now an association of almost 100 electrical installation firms who have joined together for the purpose of bringing about a common standard for installation buses in the market place. Their objective for a uniform building management system throughout Europe is achieved by: Laying down technical directives for systems and

products. Devising quality rules. Drawing up test procedures. Making system know-how available to members,

subsidiaries and licensees. Engaging test institutes to perform quality inspections. Granting third parties who pass tests the use of the "EIB"

mark. Taking an active part in standardization.


http://www.eiba.com/

EIB concentrates unequivocally on home and/or building management. This focus permits it to deal with all tasks and challenges within this domain thoroughly and efficiently. The European Installation Bus (EIB) is an open, comprehensive system that covers all aspects of Building Automation.

Though standardized Bus Access Unit (BAU) building blocks are available from several vendors. This means EIB is open.

Conformity tests are defined, and EIB Certification is open to all members of the Association.

EIB embeds the protocol in an encompassing Home and Building Electronics System, with standardized system components (such as the BAUs), network management and interworking standards, with a vendor-neutral tools and programming interfaces for PC's, training for electrical contractors, certifications schemes etc.


Communication protocols – EIBUS – An example

Decentralized

BEMS-Communication EIB

The Installation Bus is designed to provide distributed technical control for management and surveillance of buildings.

Therefore it provides a serial data transmission between the devices connected to the bus. It also operates as a compatible, flexible low-cost system supporting the above applications.

Centralized

BEMS-Communication EIB

BEMS-Communication LON

Local Operating Network Technology is a universal, open standard networking platform created by Echelon Corporation for networks control.

A LonWorks control network is any group of devices working together to monitor sensors, control actuators, communicate reliably using an open protocol, manage network operation, and provide local and remote access to network data.

In some ways, a LonWorks control network resembles a data network, such as a LAN (local area network).

Data networks consist of computers attached to various communications media, connected by routers, which communicate to one another using a common protocol.

Network management software allows administrators to configure and maintain their computer systems.

Control networks contain similar pieces optimized for cost, performance, size, and response characteristics of control.

They allow networked systems to extend into a class of applications that data networking technology cannot reach.


A LonWorks network consists of a number of nodes communicating over a number of media using a common protocol. The main parts of the network are :

The nodes, which are intelligent devices, that "talk" via the communication protocol assuring their interoperation and interaction.

Network equipment (Router, Repeater, Gateway and PC cards, Router/Modem).

Transceivers (TP, Power lines, IR, RF, FO). PC or microprocessor communications software (DDE or

MIP). Configuration, management, supervision and maintenance

software.


The main advantages of the LonWorks network are: It is a distributed control network. Easier integration of different devices

(sensors, actuators, controllers, etc.) from various manufacturers is achieved.

Higher performance due to peer-to-peer communications is ensured.

Reduced costs of installation and reconfiguration due to its distributed characteristics

Wireless networks

Large networks - large number of devices and large coverage area

Form networks autonomously and operate very reliably without any operator I intervention

Very long battery life (years off of a AA cell) Standardized protocols for interoperability Very low infrastructure cost (low device &

setup costs) Very low complexity and small size

ZigBee

Characteristics

Ad-hoc self forming networks Mesh, Cluster Tree and Star Topologies Reliable broadcast messaging Logical Device Types Coordinator, Router and End Device Trust Centre (for secure networks) Each device has unique 64 bit extended address Security Symmetric Key Authentication and Encryption Applications Device and Service Discovery Optional acknowledged service Messaging with optional responses

Devices

More….

Topologies

Applications

Applications

Home automation (HA) Low to high end residential systems for

control of devices around the home Commercial building automation (CBA)

Complete building control, monitoring and energy management

Industrial plant monitoring (IPM) Monitoring time varying attributes related to

operating environment and machinery conditions

Wireless sensor applications (WSA)

More applications

Telecom applications Light data sharing Active RFID in mobile paying

Automatic Meter Reading Residential & commercial utility systems

Personal/home health care Body area networks Fitness monitoring: home, gym, on-the-move Patient monitoring

Automotive In vehicle control: vehicular & entertainment

Status monitoring

Example : BUILDING IN CRETE

The proposed IBEMS:

Incorporates the three aspects of the indoor comfort (thermal, visual comfort and indoor air quality) in a global control strategy for building’s zone control. The control strategy maximizes the energy conservation by giving priority to passive techniques.

Integrates the occupants’ comfort requirements into the control strategy and simultaneously minimizes the energy consumption.

Can be installed either in new or in existing buildings that are the most energy inefficient.

Objectives

The Control Architecture

ZoneController

Building+

set-points

- PMVCO2

Illuminance

Fuzzy/GeneticProcedure

Userspreferences

PMVCO2

Illuminance

The Hardware Architecture

L o c a l O p e ra t in g N e tw o rk

L O O N Y

L O NM O D U L E

S m a rt c a rdk io s k

R S 4 8 5P L C 1


L O O N Y

R S 2 3 2

R S 4 8 5

P L C i


L O O N Y

R S 2 3 2

R S 4 8 5

L O NM O D U L E


R S 4 8 5

Building modelling

ZoneController

Building+

set-points

- PMVCO2

Illuminance


Userspreferences

PMVCO2

Illuminance

The zone controllers

ZoneController

Building+

set-points

- PMVCO2

Illuminance


Userspreferences

PMVCO2

Illuminance

The zone controllers

The developed zone controllers are: Fuzzy Fuzzy PID Fuzzy PD Adaptive fuzzy PD

The fuzzy controller• Inputs:

– PMV index– Outdoor temperature– CO2 concentration– Rate of change of CO2

– Indoor illuminance• Outputs:

– Heating/Cooling– Window opening– Shading– Electric lighting

• Membership functions:– Triangular and

trapezoidal• Inference engine

– min-max• Defuzzification method

– Center of area

- 3 - 2 - 1 0 1 2 3

0

0 . 2

0 . 4

0 . 6

0 . 8

1

p m vD

egre

e of

mem

bers

hip

O K V PV N O K P PN O K N

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

0

0 . 2

0 . 4

0 . 6

0 . 8

1

a h

Deg

ree

of m

embe

rshi

p

O F F V V S V S S P S P M P B V BA L M O S T O F F

The fuzzy PID controller

z -1

P I

z -1

B u ild in g

+s e t

-p o in ts

y (k )

u (p i)

u (d )

a c tu a to rs (k )

-

D-

+

+

+

+

+

e (k )

e (k -1 )

- 3 - 2 - 1 0 1 2 3

0

0 .2

0 .4

0 .6

0 .8

1

P m v e , P m v c e

Deg

ree

of m

embe

rshi

p

N e g P o sZ e

The fuzzy PID controller

- 1 0 0 - 8 0 -6 0 - 4 0 - 2 0 0 2 0 4 0 6 0 8 0 1 0 0

0

0 .2

0 .4

0 .6

0 .8

1

a h

Deg

ree

of m

embe

rshi

p

N e g Z e P o sS N S P

-100 -80 -60 -40 -20 0 20 40 60 80 100

0

0.2

0 .4

0 .6

0 .8

1

D ah

Deg

ree

of m

embe

rshi

pn eg ze ro pos

The fuzzy PD controller

z-1

PD

z-1

Building

+set

-points

y(k)

actuators(k)-

-

+

++

Gce(k)e(k-1)

Gu(k)e(k)

Ge(k)

The adaptive fuzzy PD controller

• The adaptation algorithm is based on the computation of the values z1 and z2 , which are predicted values for the two inputs e(k) and ce(k) according to a reference model.

• The reference model is a closed loop system with second order transfer function and unit feedback.

• The scaling factors Ge(k), Gce(k) are adapted such as the final values of the inputs to be as close as possible to the inputs that correspond to the reference model.

• The tuning procedure of the controller outputs presumes an indication of the influence of the control signal to the response which is reflected more accurately by the ce(k). Thus, ce(k) and its relevant scaling factor are used for the adaptation of the scaling factor of the output.

A dapta tion p rocedure

z-1

P D

z-1

B u ild ing

+set

-po in ts

y(k )

ac tua to rs(k)-

-

+

++

G ce(k)e (k-1 )

G u(k)

e (k)G e(k)

Simulation results – PMV response for 1 winter day

• The PMV reference signal used is –0.5• The fuzzy controller has no overshoot, no steady

state error and higher rise time.

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0- 2

- 1 . 5

- 1

- 0 . 5

0

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0- 2

- 1 . 5

- 1

- 0 . 5

0

PM

V

t i m e s t e p

T h e P M V r e s p o n s e o f t h e f u z z y c o n t r o l l e r

T h e P M V r e s p o n s e o f t h e f u z z y n o n a d a p t i v e P D c o n t r o l l e r

Simulation results - CO2 response for 1 winter day

Simulation results - CO2 for 1 winter day

• The CO2 reference signal used is 800 ppm.• The steady state error of the fuzzy controller (~50 ppm) is

attributed to the coupling of the indoor air quality and thermal comfort the fuzzy rules.

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 08 0 0

8 5 0

9 0 0

9 5 0

1 0 0 0

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 07 5 0

8 0 0

8 5 0

9 0 0

9 5 0

1 0 0 0

T i m e s t e p

CO

2 (ppm

)

T h e C O 2 r e s p o n s e o f t h e f u z z y c o n t r o l l e r

T h e C O 2 r e s p o n s e o f t h e f u z z y n o n a d a p t i v e c o n t r o l l e r

Simulation results – Indoor illuminance response of the fuzzy non adaptive PD controller for 1 winter day

Simulation results – Indoor illuminance response for 1 winter day

• The illuminance reference signal used is 500 lux.• The steady state error of the fuzzy controller is high (50-

80 lux) because the shading output changes slower than the indoor illuminance.

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 04 5 0

5 0 0

5 5 0

6 0 0

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 04 5 0

5 0 0

5 5 0

6 0 0

T i m e s t e p

Indo

or Il

lum

inan

ce (l

ux)

T h e I n d o o r i l l u m i n a c er e s p o n s e o f t h e f u z z y c o n t r o l l e r

T h e I n d o o r i l l u m i n a c er e s p o n s e o f t h e f u z z y n o n a d a p t i v e P D c o n t r o l l e r

Optimization procedure of zone controllers

ZoneController

Building+

set-points

- PMVCO2

Illuminance


Userspreferences

PMVCO2

Illuminance

Optimization using Real Coded Genetic Algorithms

Why GA: GAs are highly applicable to examples of large non-linear models,

where the location of the global optimum is a difficult task. Due to the probabilistic development of solutions, GAs are not restricted by local optima.

They are capable of searching solutions in a poorly understood or irregular space because they work through function evaluation rather than differentiation or other means.

GAs computational code is very simple and provides a powerful search mechanism.

Why real coded GA: When dealing with variables in continuous domains, it is more natural

to represent the genes directly as real numbers. They are more consistent from run to run. They are faster than the binary coded GAs

Genetic Algorithm Objectives

The objectives of the Genetic Algorithm optimization technique are:

(i) The occupants’ preferences satisfaction;

(ii) The minimization of the energy consumption for heating/cooling and electric lighting.

The adaptation of the fuzzy controller to the GA settings

• Neuro-fuzzy algorithm (C.T.Lin, G.S. Lee)– Only gaussian membership functions can be used.– Training data do not exist thus were developed for the application. – Unsatisfactory results:

• The neuro-fuzzy adapts the fuzzy controller to the specific training data developed for a specific building and for specific climatic conditions.

• If the building or the climatic conditions change then the fuzzy controller does not respond well.

• Shifting the membership function along the x-axis – Simple solution – Satisfactory results

Neuro-fuzzy algorithm

P M V b e fo re N F a d a p ta tio nP M V a fte r N F a d a p ta tio n

P M V re fe re n c e

T im e s te p

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0-2

-1 .5

-1

-0 .5

0

PM

V

Shifting the membership functions

0 1000 2000 3000 4000 5000 6000-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

Time step (sec)

PMV

PMV before GA optimizationPMV GA settingPMV after GA optimizationPMV users preference

Hardware architectureThe main components of the IBEMS are:• The smart card unit (MMI, users’ preferences)• The sensors and the actuators• The PLC controller and/or the LON devices • The central PC (optimization algorithm)

LOONY

LOONY

RS232

RS232

SENSORS ACTUATORSSMART CARD

UNIT

PLC RS485

ZONE 1 ZONE 2

LOCAL OPERATING NETW ORK

SENSORS ACTUATORSSMART CARD

UNIT

LONMODULE RS485

Smart card unit

H E A T

H igh

F IX - SF IX - L

V E N T I-L A T IO N

E N E R G YL IG H T

Low

H igh

Low

Smart card unit• It is mounted to the wall and reads/writes card where the users’

preferences are stored.• The card is programmed with the default values of the environmental

variables. • When the card is inserted in the unit the system detects the presence of

the user and starts its operation.• The user change his/her preferences by pressing the up or the down arrow

key on the panel. These changes are called the “delta of preferences”. The delta of preferences values vary from –3 to +3.

• Every time a delta of preference is declared by the user its value is forwarded to the PC. The PC evaluates the Short Term Preferences (STP) and uses them for the evaluation of the Long Term Preferences (LTP).

• The LTP replace the defaults on the smart card. The LTP concept minimizes the communication traffic between the smart card unit and other components of the system.

• The FIX-L and FIX-S buttons permit the user to manually fix the lighting and shading to completely ON or OFF in some special cases.

Sensors and Actuators

Sensors:• Thermal comfort sensors:

– Mean Radiant Temperature– Indoor temperature– Relative Humidity– Air flow

• Indoor air quality sensor:– CO2 sensor

• Visual comfort sensor:– Illuminance sensor

• Outdoor conditions sensors:– Outdoor temperature– Outdoor humidity

Actuators:• Relay for a/c system

– Operates on a duty cycle respective to the fuzzy controller’s output.

• Window motors:– The linear output variable of the fuzzy

controller is transformed to a digital signal with time duration respective to the fuzzy controller’s output.

• Shading motors:– The linear output variable of the fuzzy

controller is transformed to a digital signal with time duration respective to the fuzzy controller’s output.

• Electric lighting:– 3 Relays

The PLC

• Reads the sensors data through its analogue input channels.

• Runs the fuzzy control algorithm for the adjustment of indoor thermal-visual comfort and air quality levels.

• Drives the actuators through its digital and analogue outputs.

• Communicates with the smart card unit using an RS485 connection.

• Communicates with the PC via an RS232 port through the LOONY (a LON device).

Experimental results

The system is installed in Electric Circuits Laboratory of the Technical University of Crete

The installation (1)

Sensors


Window motor


Shading device


PC & PLC

Monitoring without the controller. Model Validation

T e ch n i ca l U n i ve rs i ty o f C re te - ELC IR ES La b M e a s u re d a n d e s ti m a te d te m pe ra tu re s fro m th e pe ri o d fro m 2 3 /8 /9 9 to 1 /9 /9 9

1 0

1 5

2 0

2 5

3 0

3 5

4 0

1 2 .3 01 2 .4 71 2 .6 31 2 .8 01 2 .9 71 3 .1 31 3 .3 01 3 .4 71 3 .6 31 3 .8 01 3 .9 71 4 .1 31 4 .3 01 4 .4 71 4 .6 31 4 .8 01 4 .9 71 5 .1 31 5 .3 01 5 .4 71 5 .6 31 5 .8 01 5 .9 71 6 .1 31 6 .3 01 6 .4 71 6 .6 31 6 .8 01 6 .9 71 7 .1 31 7 .3 01 7 .4 71 7 .6 31 7 .8 01 7 .9 71 8 .1 31 8 .3 01 8 .4 71 8 .6 31 8 .8 01 8 .9 71 9 .1 31 9 .3 01 9 .4 71 9 .6 31 9 .8 01 9 .9 72 0 .1 32 0 .3 02 0 .4 72 0 .6 32 0 .8 02 0 .9 72 1 .1 32 1 .3 02 1 .4 72 1 .6 32 1 .8 02 1 .9 72 2 .1 32 2 .3 02 2 .4 72 2 .6 32 2 .8 02 2 .9 72 3 .1 32 3 .3 02 3 .4 72 3 .6 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 00 .1 70 .3 30 .5 00 .6 70 .8 31 .0 01 .1 71 .3 31 .5 01 .6 71 .8 32 .0 02 .1 72 .3 32 .5 02 .6 72 .8 33 .0 03 .1 73 .3 33 .5 03 .6 73 .8 34 .0 04 .1 74 .3 34 .5 04 .6 74 .8 35 .0 05 .1 75 .3 35 .5 05 .6 75 .8 36 .0 06 .1 76 .3 36 .5 06 .6 76 .8 37 .0 07 .1 77 .3 37 .5 07 .6 77 .8 38 .0 08 .1 78 .3 38 .5 08 .6 78 .8 39 .0 09 .1 79 .3 39 .5 09 .6 79 .8 31 0 .0 01 0 .1 71 0 .3 31 0 .5 01 0 .6 71 0 .8 31 1 .0 01 1 .1 71 1 .3 31 1 .5 01 1 .6 71 1 .8 31 2 .0 01 2 .1 71 2 .3 31 2 .5 01 2 .6 71 2 .8 31 3 .0 01 3 .1 71 3 .3 31 3 .5 01 3 .6 71 3 .8 31 4 .0 01 4 .1 71 4 .3 31 4 .5 01 4 .6 71 4 .8 31 5 .0 01 5 .1 71 5 .3 31 5 .5 01 5 .6 71 5 .8 31 6 .0 01 6 .1 71 6 .3 31 6 .5 01 6 .6 71 6 .8 31 7 .0 01 7 .1 71 7 .3 31 7 .5 01 7 .6 71 7 .8 31 8 .0 01 8 .1 71 8 .3 31 8 .5 01 8 .6 71 8 .8 31 9 .0 01 9 .1 71 9 .3 31 9 .5 01 9 .6 71 9 .8 32 0 .0 02 0 .1 72 0 .3 32 0 .5 02 0 .6 72 0 .8 32 1 .0 02 1 .1 72 1 .3 32 1 .5 02 1 .6 72 1 .8 32 2 .0 02 2 .1 72 2 .3 32 2 .5 02 2 .6 72 2 .8 32 3 .0 02 3 .1 72 3 .3 32 3 .5 02 3 .6 72 3 .8 30 .0 0

8 /2 3 /9 9 8 /2 4 /9 9 8 /2 5 /9 9 8 /2 6 /9 9 8 /2 7 /9 9 8 /2 8 /9 9 8 /2 9 /9 9 8 /3 0 /9 9 8 /3 1 /9 9 9 /1 /9 9Ti m e

T [°

C]

T i n -m e as u r e d [oC ] T in -e s tim ate d [oC ] T ou t [oC ]


Technical University of Crete - ELCIRES Lab Measured and estimated illuminance from the period from 23/8/99 to 1/9/99

0

500

1000

1500

2000

2500

3000

12.3012.4712.6312.8012.9713.1313.3013.4713.6313.8013.9714.1314.3014.4714.6314.8014.9715.1315.3015.4715.6315.8015.9716.1316.3016.4716.6316.8016.9717.1317.3017.4717.6317.8017.9718.1318.3018.4718.6318.8018.9719.1319.3019.4719.6319.8019.9720.1320.3020.4720.6320.8020.9721.1321.3021.4721.6321.8021.9722.1322.3022.4722.6322.8022.9723.1323.3023.4723.630.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.000.170.330.500.670.831.001.171.331.501.671.832.002.172.332.502.672.833.003.173.333.503.673.834.004.174.334.504.674.835.005.175.335.505.675.836.006.176.336.506.676.837.007.177.337.507.677.838.008.178.338.508.678.839.009.179.339.509.679.8310.0010.1710.3310.5010.6710.8311.0011.1711.3311.5011.6711.8312.0012.1712.3312.5012.6712.8313.0013.1713.3313.5013.6713.8314.0014.1714.3314.5014.6714.8315.0015.1715.3315.5015.6715.8316.0016.1716.3316.5016.6716.8317.0017.1717.3317.5017.6717.8318.0018.1718.3318.5018.6718.8319.0019.1719.3319.5019.6719.8320.0020.1720.3320.5020.6720.8321.0021.1721.3321.5021.6721.8322.0022.1722.3322.5022.6722.8323.0023.1723.3323.5023.6723.830.00

8/23/99 8/24/99 8/25/99 8/26/99 8/27/99 8/28/99 8/29/99 8/30/99 8/31/99 9/1/99

Time

Illu

min

ance

(lux

)

Illuminance-measured [lux]

Illuminance-estimated [lux]


Experiment ModelAverage air temperature (C) 33.0 32.9

Standard deviation of air temperature (C) 1.5 0.9

Average absolute difference of air temperature (C)

0.7

Experiment ModelAverage levels of illuminance (lux) 419.1 475.4

Standard deviation of illuminance (lux) 436.9 499.7Average absolute difference of illuminance (lux) 225.8

Control with the IBEMS

-1.3

-1.2

-1.1

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.411:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

PMV

Inde

x


10

12

14

16

18

20

22

24

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

Tem

pera

ture

(ºC)

Tin MRT Tout


0

10

20

30

40

50

60

70

80

90

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

Hea

ting

coe

ffic

ient

(%)


250

350

450

550

650

750

850

950

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

[CO

2] (p

pm)


0

5

10

15

20

25

30

35

40

45

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

Win

dow

ope

ning

(%)


0

200

400

600

800

1000

1200

1400

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

Indo

or il

lum

inan

ce (l

ux)


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40

Time

Shad

ing

coef

fici

ent


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

11:16 12:28 13:40 14:52 16:04 17:16 18:28 19:40Time

Elec

tric

light

ing

coef

ficie

nt

Energy consumptionSummer Period - Cooling

Fuzzy Controller On-Off Controller

31.8 KWh/m2 37 KWh/m2

Estimated energy saving 14%

Winter Period -Heating


26.3 KWh/m2 35.8 KWh/m2

Estimated energy saving 26.5%

Annual Period-Heating/Cooling


58.1 KWh/m2 72.7 KWh/m2

Estimated energy saving 20.1 %

Annual Period-Lighting


7.3 KWh/m2 30.7 KWh/m2

Estimated energy saving 76.3 %

Annual Period-Total


65.4 KWh/m2 103.4 KWh/m2

Estimated energy saving 36.8%

Cost Analysis

The following considerations are made:• The energy consumption is 391 MJ/year/m2

• The energy savings due to the IBEMS is 137 MJ/year/m2

• 1 MJ costs 0.024 Euros.• The investment cost is approximately 4000-6000 Euros.• Global warming:

• The CO emission reduction is 10 kgr/ m2 /year.• The CO2 emission reduction is 5 kgr/ m2 /year.• The NOx emission reduction is 3 kgr/ m2 /year.

The cost analysis showed:• Energy reduction for a five year period were estimated equal to 0.14 TJ.• The reduction of the CO, CO2 and NOx emissions, are estimated 0.02,

0.01 and 0.006 x 106 kgr, respectively ten years after the installation. • The payback period is approximately eight years after the installation.

Example 2: Greenhouse

Introduction

The production management in a greenhouse demands decisions to be taken over several timescales. These decisions can be the produce together with the seed period as well as the regulation of the indoor environment with heating and ventilation among other means. Some of these decisions take place without any mechanical support and others can be taken and applied via automation systems.

A significant part of these decisions, after the selection of the produce to be cultivated, concern mainly the regulation of the microclimate. It should be noted that frequent and fast responses are often required for the system to be capable of adapting to alternating weather conditions.

The regulation of each of the factors influencing the indoor environment of a greenhouse is a dynamic, procedure. For example, the plant’s response to the indoor temperature depends on the intensity of the solar radiation, which alters continuously over time. It is also obvious that the desired temperature can not be constant over the entire cultivation period.

Parameters influencing greenhouses In advanced greenhouse automation systems, the regulation of the interior

environment is focused on creating the most appropriate microclimate for the maximization of plant growth and the reduction of the final cost, aiming for the highest possible economic profit to the business. Today with an automation system the factors that are usually regulated are: The temperature, using heating, ventilation and cooling (water-fogging,

hydropanels, etc.) The relative humidity, using also the heating, ventilation and cooling

systems. The water in the ground taking into consideration the irrigation system. The fertilizers in the ground taking into consideration the fertilizer system. The CO2 concentration, by integration of a CO2 enrichment system. The lighting taking into consideration both the artificial lighting as an

electrical load and the natural light as controlled by shading devices. Energy efficiency in greenhouses can be achieved with the following

means: With the reduction of energy loses through optimum control. With the increase of the plants’ production per unit energy consumption.

Aims and objectives

The aim of the present work is to analyze the development of an indoor environment management system for greenhouses where the following indoor environmental variables are considered and regulated via fuzzy logic control: (i) indoor temperature; (ii) relative humidity; (iii) indoor lighting and (iv) CO2 concentration.

The system’s response is tested via a TRNSYS model for greenhouses.

A detailed analysis of the system’s performance was carried out after its installation in a real greenhouse in Chania, Greece.

The interconnection between the various devices in the greenhouse is based on the Lonworks protocol.

An analysis of the greenhouse installation monitoring is incorporated.

Greenhouse model

Model assumptions

The whole space is represented by a single temperature, humidity, etc. value. In reality there are variations within the enclosure that can be modeled using computational fluid dynamics.

During the course between two successive time-steps, the change in the system parameters like temperature and humidity is linear. This is because of the way TRNSYS integrates the differential equations.

Due to the above, differential equations can be converted to simple differences. It is assumed that plant photosynthesis does not influence its heat balance.

Plant physical parameters are considered equivalent to that of water for thermal analysis.

All surfaces are assumed to be black for long-wave radiation. The long-wave radiation exchanges are calculated based on the previous

time step surface temperatures. Radiative gains from lights and people are considered negligible. The

zone, floor, plants, and equipment temperature variations are considered to be linear over each simulation time step. The air flow rate due to infiltration is calculated based on the zone temperature of the previous time step.

Model validation

Control of the greenhouse

Night cycle

During the night the photosynthesis process is dormant, a fact that the controller can take into account to further reduce energy consumption.

In this case the greenhouse microclimate has to be set into a different maintenance mode.

The internal temperature set-point is changed to a lower one. The lower temperature set point value achieves better energy consumption and is introduced because during the night it is not necessary to maintain the higher temperature that the plants need for the photosynthesis process.

The same applies for the [CO2] enrichment regulation. For the above reasons a night cycle control is introduced for the greenhouse microclimate control.

Night cycle

System’s configuration

Inputs

INPUTS

Inputs Characteristics

Outside Temperature sensor LS-T01 LONWORKS sensor Range -15..55 ?C

Iluminance sensor Analog sensor 0-10V Range 0-200000 lux

Connected to LonWorks via Analog Input module

CO2 concentration sensor (part of 3 in 1 Nose3 sensor )

Lonworks sensor Range 0-5000ppm

Humidity sensor (part of 3 in 1 Nose3 sensor )

Lonworks sensor Range 0-100%

Temperature sensor (part of 3 in 1 Nose3 sensor )

Lonworks sensor Range -10-50 ?C

Outputs

OUTPUTS

Actuators Characteristics

Electric Lighting On-Off, Connected to LonWorks via Digital Output (DO) module

CO2 enrichment On-Off, Connected to LonWorks via Digital Output (DO) module

Heating On-Off, Connected to LonWorks via Digital Output (DO) module

Cooling (Water fogging) On-Off, Connected to LonWorks via Digital Output (DO) module

Side Windows On-Off, Connected to LonWorks via Digital Output (DO) module

Roof Windows On-Off, Connected to LonWorks via Digital Output (DO) module

Shading On-Off, Connected to LonWorks via Digital Output (DO) module

Configuration in lab

Installation in greenhouse

Results Temperature in winter

Results: Temperature in summer

Results: Illuminance

Results: CO2

Problems

The heating system is operated adequately while there is no cooling system installed.

The [CO2] enrichment procedure is not cost effective for the whole year as it is quite expensive and has to be operated carefully.

The fogging system presented occasional maloperations and malfunctioning.

The shading devices cover only specific parts of the greenhouse, therefore the system was unable to fully shade the greenhouse.

Conclusions

This greenhouse control seems to be quite efficient by achieving the desired factors that any cultivation demands.

The testing of the controller through modeling shows that the set-points can reach the desirable values.

The system is further redesigned and improved through the consideration of the continuous measurements among the seasons.

The most underlying characteristic is that this control is universal and can be used in any cultivation by setting the desired factors that a grower wants.

Example 3: EIB

Test chamber

The test chamber is constructed in order to test and/or compare various sophisticated controllers supporting BEMS for the adjustment of the indoor environmental characteristics before their final installation in real buildings.

The variables that influence indoor comfort are: thermal comfort, visual comfort and indoor air quality.

Thermal comfort depends upon indoor temperature, relative humidity, air movement and indoor surface temperatures. Additionally thermal comfort depends upon subjective parameters such as clothing, metabolism and activity level. The first four parameters are measured, while subjective parameters are estimated on the basis of the building type and activities.

Visual comfort depends upon indoor illuminance levels (monitored). Finally, indoor air quality depends on the CO2 concentration inside a building

(monitored). The various components i.e. sensors, actuators and interfaces are

interconnected using specific devices for the EIB protocol and the EIS standard (EIB Interworking Standard). The intelligent controllers are developed in Matlab in order to be easily programmed, tuned and updated. The interconnection between MATLAB and EIBUS is performed via the OLE for Process Control (OPC) Standard.

Test chamber

EIB devices

E-Building platform

The e-building platform

The e-building platform is an add-on to existing Building Energy Management Systems (BEMS) and performs the following actions: Indoor environmental parameters monitoring and checking

occupants’ indoor environment satisfaction (thermal, visual, acoustical comfort and indoor air quality).

Formulation of a database sensors’ protocol related to building’s environmental and energy performance.

Checking of energy bills and performing energy labeling and certificate.

Environmental labeling and certificate. Creation of electronic forms for filling in the requested information

by occupants and energy managers. Evaluation and categorization of possible scenarios for

improvement of indoor comfort and/or energy efficiency. Decision support for buildings’ energy managers. Propose possible measures that improve energy and

environmental labeling of buildings.

The e-building platform

The E-building platform, which is based on the IBM LOTUS DOMINO environment and has been developed in JAVA is consisted of the following subsystems: Electronic Data Transfer

Subsystem. Data Storage and

Processing Subsystem. The Decision Support

Subsystem. WEB Interface

Subsystem

Data Storage Subsystem

All BEMS collect data from sensors that are usually transferred in a database. The necessary measurements are: Indoor temperature Indoor relative humidity CO2 concentration Indoor lighting levels Energy consumption for heating cooling and lighting.

The Electronic Data Transfer Subsystem transfers the collected data from the BEMS to the e-building platform for further analysis.

The storage subsystem uses the stored data for the energy rating and environmental rating of each building under investigation.

Decision support subsystem Although decision support is essential for adopting decisions,

almost all decision processes include uncertainty. There is an important difference between a “good” decision and

in a “good” result in the process of decision-making under uncertainty.

The statistical analysis of decision making systems is based on the fact that the future uncertainty of the various actions’ impact can be statistically characterized. The outcome of the decision problem’s solution is divided to a number of states.

In the energy auditing problem these states are the energy labels that will be attributed to a specific building after the examination and the analysis of the alternative scenarios.

Decision support subsystem

For energy auditing the future states and their prior probabilities are defined in the Table below based on the analysis of statistical data of buildings in Greece.

The scenarios for improving the energy rating that are proposed are: Increased insulation External finishing with high reflectance Replacement of windows with low-e panes. Use of ceiling fans Change of heating and cooling temperature settings Improvement of the HVAC’s efficiency

Future states Percentage of total buildings Prior probabilitiesΑ 0-30 % 0.3Β 30-50 % 0.2C 50-75 % 0.25D 75-100 % 0.25

Web interface screenshots

Installation and testing

Testing in BYTE’s building: A construction with a series of reflective window panes. The building is divided into two main areas: the new

building and the existing building. There are mainly offices except from the basement which is

used for storage. There are rooms specifically used for servers.

The installed Building Energy Management system is a Lonworks network.

The main equipment of the BYTE premises consumes electricity: (i) VRV air conditioning system; (ii) lighting; (iii) Generators and (iv) elevators; (v) fire security and safety systems; (vi) water and sewage pumps.

Installation plan

Indoor temperature

0

5

10

15

20

25

30

4/12/050:00

14/12/050:00

24/12/050:00

3/1/060:00

13/1/060:00

23/1/060:00

2/2/060:00

12/2/060:00

22/2/060:00

4/3/060:00

Date and Time

Indo

or te

mpe

ratu

re (o

C)

Temperature Ground Floor Zone 3 -New buildingTemperature Ground Floor Zone 1 -New buildingTemperature Groun Floor Zone 2 -New building

Indoor thermal comfort

-1,2

-1

-0,8

-0,6

-0,4

-0,2

021/11/200

5 0:001/12/2005

0:0011/12/200

5 0:0021/12/200

5 0:0031/12/200

5 0:0010/1/2006

0:0020/1/2006

0:0030/1/2006

0:009/2/2006

0:0019/2/2006

0:001/3/2006

0:00

PM

V in

dex

Indoor air quality

0

200

400

600

800

1000

1200

1400

1/12/050:00

11/12/050:00

21/12/050:00

31/12/050:00

10/1/060:00

20/1/060:00

30/1/060:00

9/2/06 0:00 19/2/060:00

1/3/06 0:00

[CO

2] (p

pm)

Energy consumption

0

500

1000

1500

2000

2500

Recep

tion-C

argo-B

asem

ent

Meetin

g Roo

m Grou

nd Floo

rRes

tauran

tCarg

o

Zones

2 & 3

of the

1st fl

oor

Zones

4 & 5

of the

1st fl

oor

Server

room & zo

ne 2

of the

seco

nd flo

or

Zones

3 an

d 4 of

the 2

nd flo

or

Zones

2 & 3

3rd flo

or

Zones

4 & 5

3rd flo

or

Zones

2 & 3

4th flo

or

Ζone

s 4 & 5

4th flo

or

Ζone

s 1 & 2

6th flo

or

Ζone

s 1,2,

3 of

the 7t

h floo

r

Ζone

s 4 & 5

7th flo

or

Ζone

s 3 & 4

6th flo

or

Ζone

s 3 & 4

5th flo

or

Ζone

s 5 & 6

5th flo

orEn

ergy

Con

sum

ptio

n(kW

h)

Energy consumption

BYTE is consuming around 135 kWh/m2 per year. Based on the analysis performed by the e-building

platform the energy consumption for heating during the monitoring period is almost 16% of the total energy consumption.

Therefore BYTE consumes almost 85% of its energy consumption for electric lighting, appliances, PCs, etc. This is explained by the company’s activities which is mainly focusing on software and computer applications.

Conclusions

The e-building platform is a software application for energy and environmental rating of buildings while simultaneously monitors the energy performance and proposes solutions for improvement of the energy rating of buildings.

The platform is tested in BYTE’s building: The indoor thermal comfort and indoor air

quality is maintained within acceptable levels during all the monitoring period.

Intelligent buildings global assessment method

Performance indicators Five global performance indicators (GPIs) are

specified and used, each of these consisting of five specific performance indicators (SPIs) which belong to one of five spheres of influence. The adopted GPIs are: Built Environment Responsiveness Functionality Economic issues Suitability

Specific performance indicators People: Comfort and productivity, How well do they understand their

relationship with the building, do they have a role in the energy management, etc.

Systems: facilities for individuals to change the set-point of local devices, the building and its systems is well integrated with the surroundings, etc.

Critical: Facilities equipped to handle emergencies, maintenance, treatment of the waste and use of renewable energy sources, etc.

Process: The technical competence of the facility managers, organisational regime of dealing with energy related issues, etc.

Design: Urban and building internal planning, Design considerations and decisions on possible change of partitions, layout and services systems required by the change of usage?

Assessment

Each of the five GPIs are influenced by the five spheres of influence.

Each of the performance indicators has a value ranging from 0 to 5, with 5 indicating the best and 0 indicating the worst. The overall assessment scheme which results in the building IQ.

The rating of the intelligent building is accordingly specified as follows: Bad: <50 Good: 50 ~80 Very Good: 80 ~100 Excellent: 100~125

Built Environment (PE)

Responsiveness (PR)

Functionality (PF)

Economic (PE)

Suitability (PS)

(gE)

(gR)

(gF)

(gE)

(gS)

Weighting Factors for individual PI

Weighting Factors for Overall PI

Stage 1: Assessment scheme

Stage 2: Individual PI Stage 3: Overall assessment

Overall Intelligence

of the building

(IQ)

Sub-performance indicators: Comfort and productivity: at what level that the building

creates a comfort environment for the occupants Individual control of local environment: can individual

occupant change the set-point of their terminal devices such as fan-coil unit or solar shedding devices

Health and safety: is it safe and health for people to stay in or around the building

Energy consumption and environmental impacts: is there an organisational policy on the operation of the built environment and the associated environmental impacts

Integration with the surrounding ecological systems: how are decision made during the design phase regarding to macro-climatic design, building integrated renewable energy sources and rainwater/wastewater utilisation.

Built Environment

Awareness: how well the relevant people understand their relationship with the building

Automatic response to changes in the surroundings: is there any measures that allow the building appropriately responds to the changes in the surroundings, utility supply, services systems and usage of the buildings.

Performance under emergencies: what level of emergencies can be handled within and around the building

Decision-making: the ability of building operators to make decisions in responding to changes

Flexible usage: is it flexible to alter the partitions, layouts and services systems for different usage.

Responsiveness

Functionality

Reporting system: how well the information associated with the efficient management and operation of the building is communicated to the relevant parties.

Building Management System (BMS): is there a BMS installed and how is it being used.

Maintenance: how the building, including architectural features, BMS (if any), and services systems, is maintained.

Facility Management (FM): is there a facility manager or management team and how technically competent are they.

Easy-to-use through design: how the issues related to the ease of use is considered in the design phase. `

Economic issues Investment: are the intelligent building technologies are

valued by the relevant decision makers Energy supply: how easy (or difficulty) is it to change the

supply of energy Resources (water, waste treatment, etc): how energy

audit, monitoring of water usage, and waste treatment are carried out

Costs: how the operating cost associated with energy and other utilities are paid by tenants

Budget: what procedure is employed to determine the ratio of the initial construction cost to the lifecycle cost

Suitability

Special use: does the building provide features to satisfy special needs of some individuals such as the disabled or elderly.

IT connectivity: does the building have access to specialist services providers through IT network.

Location: is the building located such that the activities within the building have easy access to the relevant sources

Organisation: is there an appropriate communication between different divisions of an organisations that allow effective dissemination of information associated with efficient operation of the building

Internal flow and operational planning: what process or method are employed in the design phase to make decisions associated with the location of interacting divisions in the building and the movements of staff and information.

Evaluating Intelligent Buildings – Matrix Tool -Factors

These performance indicators are influenced by a number of factors. This Matrix Tool only consider the five factors, as follows:

1. People Do they feel comfort and are they productive in the building How well do they understand their relationship with the building Do they have a role in the energy management Investment decision-makers: do they understand the benefit of intelligent building

technologies and are they willing to investigate the feasibility of relevant investment People with special needs (such as the disabled and elderly): can the building satisfy their

special needs2. Building systems

Does the system provide facilities for individuals to change the set-point of local devices according to their desire

Are the building and its systems well integrated with the surroundings Is the building controlled and managed by a Building Management System (BMS) Is it technically feasible to change the suppliers of utilities when considered beneficial Does the building have good access to the internet.

Evaluating Intelligent Buildings – Matrix Tool -Factors

3. Critical What measures are there to ensure the safety and health of people staying in and around the

building Facilities equipped to handle emergencies Maintenance and services of the facilities equipped to handle emergencies Treatment of the waste and use of renewable energy sources The factors associated with the location of the building that affect the performance of the building

under emergencies4. Process

The process of adapting energy management policies within the organisations The technical competence of the building operators in dealing with any relevant change The technical competence of the facility managers Facilities for individual tenants to control and meter their utilities Organisational regime of dealing with energy related issues

5. Design Design considerations and decisions on the integration of the building and its systems with the

surroundings Design considerations and decisions on possible change of partitions, layout and services systems

required by the change of usage The use, operation and maintenance of building systems Decision on the initial and lifecycle costs Urban and building internal planning

Evaluating Intelligent Buildings – Matrix Tool

Intelligent Buildings TechnologyIntelligent Buildings Technology

Special Needs of Users

Connectibility (C.I.B.) Location Organisation

Interior Operations

(Flows) SUITABILITY

Investment Power Supply

& Fuel Options

Overall Resource

Management Cost

Centres Construction/Rent

Cost ECONOMIC

Reporting Building

Management System

Maintenance Facilities Manager

Ease of Use

FUNCTIONALITY

Awareness Automated Components Emergency

Devolved Decision Making

Spacial RESPONSIVENESS

Comfort Local Interface Health Policy Sustainability BUILT

ENVIRONMENT

PEOPLE SYSTEMS CRITICAL PROCESS DESIGN

The toolkit

A software is developed for the evaluation of the PIs by experts.

MATRIX_TOOL

Case studies: Aggelidis - Georgakopoulos

Aggelidis – Georgakopoulos building The present building is located at Krioneri, Athens- Lamia, It is a mixed-use building, offices and storage spaces; The building was constructed in 2002, to accommodate the increasing needs

of the company, focusing on flexibility and safety, as well as to provide excellent comfort conditions for its users.

Office area 900 m2, located on the first floor of the building. The rest of the building consists of storage places of paper, an area of 10000 m2. The gross floor area of 10900 m2 is distributed on three levels, basement, ground floor and one office floor.

Aggelidis – Georgakopoulos Features Designed to exploit natural lighting potential of the

area Skylights, ceiling funs Buried pipes on the storage places Shading

Black out blends (workstations) Semi-transparent internal rollers (all other places)

Central Building management system (BMS) HVAC Power Generator

Aggelidis - Georgakopoulos Impact on People The impact of people is

excellent on Built Environment and Responsiveness, while Functionality and Economy have also achieve high scores;

The only declination is on Suitability; in this issue, typically the building can not have a higher score, as there is no specific design for disabled people.

Impacts of People

012345

Env ironmental

Responsiv eness

FuncionalityEconomic

Suitability

Aggelidis - Georgakopoulos Impact on Systems The impact of systems

presents a remarkable declination in the Built Environment. This is due to the fact that only central control is applied.

All indoor comfort indices are directly controlled from the BMS, and occupants have no control on their immediate indoor environment.

Impacts of Systems

012345

Env ironmental

Responsiv eness


Suitability

Aggelidis - Georgakopoulos Impact on Critical

Critical issues impact is excellent in terms of Built Environment and Functionality, while all other performance indicators have also achieved big credits.

Impacts of Critical Issues

012345

Env ironmental

Responsiv eness


Suitability

Aggelidis - Georgakopoulos Impact on Process

Process impact is positive on all performance indicators and excellent in terms of Functionality.

Impacts of Process

012345

Env ironmental

Responsiv eness


Suitability

Aggelidis - Georgakopoulos Impact on Design Both, the impact of

processes and design are excellent on Functionality.

This depicts that the specific issue was the prime concern during the design phase and holds a key role in the building use and operation

Impacts of Design

012345

Env ironmental

Responsiv eness


Suitability

Aggelidis - Georgakopoulos

The score of 94, rates the building in the very good category.

All performance indicators, except systems, have achieved good credits.

As mentioned, this is due to the fact that only central control is applied.

There is potential for improvement, as the building has the ability to give the override to users, but it is a question of internal management and decision-making. Furthermore, there are no complains of the users concerning the way they perceive their indoor environment.

Overall

05

10152025

Env ironmental

Responsiv eness


Suitability

Case studies: Tobazis Meletitiki The present building is located at

Polydrosso, Halandri in the northern part of Athens.

The building was constructed in 1995, to accommodate the architectural group A.N. Tombazis and Associates – Meletitiki Ltd. Designed by the architect Alexandros Tombazis,

The building reflects the philosophy of bioclimatic design, while promote the professional identity of the group and provides excellent comfort conditions for its users.

The building is a unique space, in terms of interior design, space manipulation and daylight design and comfort

Meletitiki:Features

Designed to exploit natural lighting potential of the area

Shading Solar fins (vertical glass silk-screen-printed panels) Fixed horizontal metal grills External venetian blinds

Passive/Hybrid cooling techniques Night ventilation Ceiling fans Cold storage system (ice banks)

Central Building Energy management system (BEMS)

Meletitiki: Impact on People

The impact of people is excellent in all performance indicators except Suitability; in this issue, there is a potential of improvement, especially for disabled persons, although the interior design, with a unique esthetic result, is clearly oriented to serve daylight and space manipulation purposes.

This fact does not facilitate services for disabled persons.

Impacts of People

012345

Env ironmental

Responsiv eness


Suitability

Meletitiki: Impact on Systems

The impact of systems is positive and balanced in all performance indicators;

This depicts that the building systems serve in a very good level the needs of its users, and in practice, there is no necessity for improvement.

Impacts of Systems

012345

Env ironmental

Responsiv eness


Suitability

Meletitiki: Impact on Critical

Critical issues impact, excellent in terms of Built Environment and Economy, have also a positive impact in Functionality and Suitability.

There is only a small declination in Responsiveness.

Impacts of Critical Issues

012345

Env ironmental

Responsiv eness


Suitability

Meletitiki: Impact on Process

Both, the impact of processes and design are very positive.

In practice, the design of the building and all process related to the day to day operation of the building are of a high level, thus there is no need for improvements.

Impacts of Process

012345

Env ironmental

Responsiv eness


Suitability

Meletitiki: Overall

All performance indicators have achieved very high credits.

The credit of 103.5 rates the building in the excellent category, meaning that the building is excellent in integrating its systems, services and management.

Overall

05

10152025

Env ironmental

Responsiv eness


Suitability

Conclusions

The tool can be adjusted with weights in the various PIs.

More information can be found in http://www.ibuilding.gr

http://www.ibuilding.gr/

Demand Side management example

EMS Design ConsiderationsEMS Design Considerations

The EMS has to:

Monitor and control the production units Monitor and control the storage units Manage the consumption units and Monitor the power network to ensure stability and

proper power quality.

EMS Design EMS Design ConsiderationsConsiderations

The proposed EMS uses partially central and partially distributed control:

Central control is provided for the production units, the storage units and the big consumption units. This part is called Central EMS (CEMS). A computer system hosts the control part of the CEMS.

For the rest of the consumer units, Automatic Distributed EMS (ADEMS) control is proposed for small consumption units.

An energy system with CEMS and ADEMS control

Production Units

Power Distribution Network

StorageUnits

Big (public used)

Consumption Units

Small (privately used)Consumption

Units

CEMS(EMS)

ADEMS(EMS)

CEMSCEMSThe CEMS must be designed for maximum

performance. Two samples of the many possible scenarios would be:

1. The production units are producing more than the current energy demand. Then the excess energy is stored. The CEMS controls in which storage(s) units the excess energy will be stored.

2. The production units are providing energy, while all storage units are totally full. The CEMS turns-on the big consumption units, if applicable.

ADEMSADEMS

Consists of a number of Intelligent Controllers (IC), where each one automatically switches a load ON or OFF.

Each IC continuously monitors the power grid characteristics and extracts information about the present and previous state of the power network.

IC takes one actions for its load: to connect it, disconnect it, keep it connected, or keep it disconnected based on: (a) the voltage magnitude, (b) the frequency, (c) the history of the voltage signal over the last few minutes, (d) the importance (priority level) of the load, and (e) the time interval.

GoodLoad =On

BadLoad= Off

GetReadyLoad= Off

MediumLoad= On

Power Up

PQ=GoodT:=Tcp

PQ<>Good

T:=T-1PQ=Goodand T>0

PQ=Goodand T=0

PQ=Good

PQ<>Good

T:=T-1

PQ=Medium

T:=Tdp

PQ=Good PQ=Mediumand T>0

PQ=Bad

PQ=Medium and T=0or

PQ=Bad

STATES LOAD

GetReady Off

Good On

Medium On

Bad Off

Electric grid quality control

(Source(Source:: E. Antonidakis, E. Antonidakis, et al, Renewable Energy Sources Congress et al, Renewable Energy Sources Congress ‘‘Towards 100% RES at islands and remote sites, Towards 100% RES at islands and remote sites, 2001.)2001.)

The smart controller

Home consumption model

Grid connections model

205

210

215

220

225

230

1 3 5 7 9 11 13 15 17 19 21 23

Time of the Day (h)

Line

Vol

tage

(Vrm

s)

With IC'sWithout IC's

Ημερήσια διακύμανση τάσης

((ΠηγήΠηγή: : E. Antonidakis, E. Antonidakis, et al, Renewable Energy Sources Congress et al, Renewable Energy Sources Congress ‘‘Towards 100% RES at islands and remote sites, 2001.)Towards 100% RES at islands and remote sites, 2001.)

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (h)

Pow

er D

eman

d (W

)

RangeWater HeaterRefrigeratorLighting & misc.Air-conditioner

05000

10000150002000025000300003500040000450005000055000

1 3 5 7 9 11 13 15 17 19 21 23Time (h)

Pow

er D

eman

d (W

)

RangeWater heaterRefrigeratorLighting & misc.Air-conditioner

Daily energy demandNo controlNo control ControlControl

Daily voltage variation

205

210

215

220

225

230

1 3 5 7 9 11 13 15 17 19 21 23

Time of the Day (h)

Line

Vol

tage

(Vrm

s)

With IC'sWithout IC's

Voltage of the grid without control

Voltage of the grid with control

A java platform for energy and environmental rating

Objectives

The software platform evaluates and performs environmental and energy classification for buildings based on national standards (e.g. CEN).

This software was developed in Java programming language using Netbeans IDE under Linux operation system. J

ava language was chosen because: a) it provides a stand alone application which may be executed under several operation systems (Windows, UNIX, Macintosh and Solaris), b) it helps on developing an open source application.

The idea of an open source application allows the improvement and expansion of the application by other members of the scientific community.

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