efficient design incorporating fundamental

EFFICIENT DESIGN INCORPORATING FUNDAMENTAL IMPROVEMENTS FOR CONTROL AND INTEGRATED OPTIMISATION.

A. Galata1, N. Morel2, J.B. Michel3, S. Karki4, L. Bakker5, H.P. Joergl6, A. Franceschini 7, A. Martinez8 1

CONPHOEBUS – Energie Rinnovabili, Risparmio Energetico, Zona Industriale, Passo Martino - 95030 Catania, Italy. EPFL, LESO-PB – Ecole Polytechnique Federale Institut de Tecnique du Batiment, 1015 Lausanne, Switzerland. 3 CSEM – Centre Suisse d’Electronique et de Microtechnique SA,Rue J. Droz,1 – 2007 Neuchatel, Switzerland. 4 VTT – Building Technology, P.O. box 100,Lampomiehenkuja, 3, FIN-02044 ESPOO, Finland. 5 TNO BOUW – Building and Construction Research, Langewaterstraat, 5 – 2628 CA Delft, The Netherlands. 6 IMPA – University of Technology Vienna, Gusshaustrasse 27-29 – 1040 Vienna, Austria. 7 SENAMION AUTOMAZIONE – Via Vetreria, 1 – 22070 Grandate (Como), Italy. 8 SGS THOMSON – Stradale Primosole, 50 – 95121 Catania, Italy. 2

ABSTRACT Operating a building efficiently means co-ordinating the physical plants with constantly changing needs. Building energy management is performed in different ways, on European scale, since design, manufacture, engineering, installation, commissioning and maintenance processes are contemporary involved, and it reflects the wide variations in climate, building type, space per capita, control and management methods, and national regulations. Optimisation and integration through smart building control systems can save substantial amounts of energy while improving requirements for indoor comfort. The EDIFICIO project, funded in part by the EU Commission in the frame of the JOULE III Programme, has the goal to develop innovative, adaptive, integrated control systems for the optimal energy management and indoor comfort in buildings. This is achieved by using Soft Computing Techniques (SCT), specially Fuzzy Logic (FL), Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs).

KEYWORDS EDIFICIO; Building Automation; Building Energy Management Systems; Energy Saving; Energy Consumption; Indoor Comfort; Fuzzy Logic; Neural Networks; Genetic Algorithms.

INTRODUCTION Recent developments in computer technologies and advanced building design(1) are merging together to offer an optimal control and management of the energy functions. To improve energy saving and rational use of energy, it is arising the idea to treat a building as a whole, as opposite to consider the building energy management for local energy functions, often implemented as single control system. Literature [1] shows a combination of different control strategies allows an efficient management and a consistent control processes co-ordination of building energy services. A study carried out in Italy [2] demonstrated a potential of energy savings around 18% for the HVAC management all over the year, and around 52% for lighting, when integrated control strategies for heating, cooling and artificial/natural lighting are adopted in place of the individual strategies. A study carried out in Switzerland [3] for the use of Fuzzy Logic blind controllers has shown that more than 10% of energy saving is possible compared to a "well-behaving" user, and more than 30% compared with more usual behaviour like keeping the same blind position during the whole day. Moreover another study [4] in Switzerland,

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living spaces, technological plants, services, office automation

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devoted to the elaboration of a smart heating controller using artificial neural networks and an optimal control based on a cost function, has shown an expected heating energy saving between 11% and 17%. Energy saving is not the only aspect to consider in building energy management. Indoor comfort and people’s quality life in living or workspace has the same importance of energy consumption. Integration of energy plants (HVAC, lighting) with envelope components (windows, entrances, sun breakers, shading devices, advanced glazing) and human behaviour (user presence and wishes), within the controlled environment, is nowadays one of the main issues for building designers. Human “pleasure requirement” such as human behaviour, user wishes and room occupancy, are to be evaluated together with physical variables such as temperatures, solar radiation, shading devices positioning, in order to obtain the best compromise between energy saving and individual comfort.

OBJECTIVES OF THE EDIFICIO PROJECT The EDIFICIO project establishes an innovative approach to realise an integrated smart control system for heating and cooling, lighting, indoor air quality and ventilation, by using SCT. The project is carried out by a team of leading institutes and companies in the field of rational use of energy and renewable energy, control sector, building automation, microelectronics, which will ensure both a high scientific level for the applications of the new techniques and a broad platform for the project results imp lementation in commercial building and control sectors. The starting point of the project is the assessment of standards for the design of adaptive control strategies, defining operative conditions (climates, building, building installations, human behaviour), performance criteria and identification of best areas of application (building types and other conditions). SCT will used to develop the new adaptive control modules. Individual controllers for heating, cooling, lighting, ventilation will be incorporated into a multifunctional control system and implemented in a fully dedicated high speed Fuzzy Logic Processor(2). The Fuzzy Logic Processor will be integrated, through a new communication protocol, into a LonWorks® BEMS network, based on Echelon technology. The performances of the new smart control systems will be tested and evaluated through computer simulation, scaled-down measurement (real time building emulator), and full-scale experiments in occupied buildings located in North, Centre and South Europe, in order to assess energy saving and indoor comfort under different: weather conditions, building occupancy and user behaviour. Short-term weather predictions and forecasting capabilities of the energy management system will be directed to anticipate delay and building plant response time. Predictions will be used as input for the control strategies and will be combined in an integrated control system. Through predictive controls it is possible to reduce the number of sensors/connections, preserving or improving the control strategy performances; a cost dropping activity that has not been investigated yet. Adaptive control strategies allow to create self-tuning building energy management systems, to solve the practical problems of badly tuned building installations.

THERMAL CONTROL Figure 1 shows the thermal control concept based on the main neuro-fuzzy modules. The climate prediction module predicts external temperature and global solar radiation for a further time horizon(3), based over a set of actual meteo measurement. Future building behaviour is established by the building behaviour prediction, which receives the previous states of the building and predicted climate data.

2 3

The Fuzzy Programmable Board (FPB) including WARP® and ST6 microchips, produced by SGS-THOMSON. i.e. 6, or, 12, or …. Hours.

2

Time External Temperature, Solar Radiation Flow Temperature

Climate Prediction

ReturnTemperature

Kp

T_int

P_opt

-

schedule

room

occupancy and modification of user’s temperature setpoint determine prediction of comfort temperature, which corresponds to the output of the user adaptation block .

-

U

The optimal control and the device control evaluate all

Building Behavior Prediction

Optimal Control

Device Control

User interface block

P

the

predictive

and

room

measured data to calculate the optimal power for the next time step.

Heating Power Sample Vector

T_comfort presence

User Setpoint, Window Opening Time schedule, user acceptance

The

choice

to

adopt

predictive control strategy,

User Behavior Prediction

combined

with

non-linear

modelling of building, user’s

Figure 1 – Neuro-fuzzy approach of thermal control.

behaviour,

and

weather

prediction, allows the thermal controller to achieve energy consumption optimisation while ensuring thermal comfort.

VENTILATION AND INDOOR AIR QUALITY CONTROL Figure 2 depicts the block-diagram of ventilation and indoor air quality neuro-fuzzy control concept. Focal point is the Decision

Weather prediction Blind position Heating / cooling Actual weather

Engine Module which uses several inputs to determine Fuzzy system creating ventilation setpoints (not real-time)

Actual wind

Occupancy predictor Occupancy

Setpoints for ventilation

Adaptive Building Model

Predicted: Tindoor Air quality

Decision Engine

Signal

Quantity

the

optimal

actions. First input is a group of possible

Real Building

ventilation

information

to

predict the building response using Neural Network. Second input is a set-point vector

User interface

Tindoor + Air quality

for

ventilation,

to

prescribe a strategy for the progress of ventilation for

Possible actions for ventilating

further

time

horizon,

generated by a fuzzy system and based on weather and Figure 2 –Neuro-fuzzy approach of Ventilation and IAQ control.

occupancy prediction. Next inputs are the actual occupancy and user’s wishes

to change the set-points in order to get high comfort in the room. The decision engine output is the control requirement for ventilation.

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LIGHTING CONTROL The proposed neuro-fuzzy lighting controller manages both daylight and artificial lighting system. It operates instantaneously when user is in the room, to adapt

immediately

lighting

and shading systems to any changing condition, in order to

ensure

working

a

convenient

plane

illuminance

and to protect the user from glare or contrast effects. The

neuro-fuzzy

approach

illustrated in Figure 3 uses two rule bases defining the blind

position

and

the

artificial lighting level, taking into account both thermal and visual aspects. Additionally, improvements are achieved Figure 3 –Neuro-fuzzy approach of lighting control.

by using neural network to learn room's user behaviour

or user wishes (i.e. dark, normal, bright), so to adapt the rule base coefficients.

SMART INTEGRATED CONTROL SYSTEM Integration, in a co-operative way, of thermal-lighting control and room occupancy allows the best exploitation of internal solar gain and overheating prevention; integration of thermal-ventilation control and room occupancy greatly reduces ventilation loss respect to constant air change rates. Basically, the structure of the integrated control system (Figure 4) results from the parallel disposition of different control loops. The integrated software control concept for heating-cooling-ventilation and lighting systems is realised considering three nested loops. First loop (inner loop) realise a high level control concerned with the device controller for translating the physical requirement(4) into physical equipment values(5), using dedicated microcontrollers(6). Second loop (rule-based control loop)is the fuzzy logic controller(7), using rules for producing an adequate level (fuzzy information) of thermal and lighting power, air change rate, blind position and slats orientation, as set-points for the first loop controller. The third loop allows a tuning of the rule base, taking into account the adaptation to the real building and weather data conditions, and to the user requirements and wishes. Adaptation is based on the proposed cost function (eq. 1), which takes into account all the partial requirements: energy consumption and thermal, visual, and air quality discomforts. J = Chc • Phc + Ce • Pe + Ct • ft (Tinside, ...) + Ca • fa (Qair, contam, ...) + Cl • fl (θ, El, ...)

(eq. 1)

where Chc, Ce, Ct, Ca and Cl are the coefficients weighting the various inconveniences to include in the cost function, Phc is the average heating/cooling power during the prediction time horizon, Pe is the electric power used for the artificial lighting, ft is a function giving the thermal discomfort in function of inside air temperature 4

i.e. the HVAC system with its internal temperature, or the blinds with both the blind position and the angle of the slats. Opening fraction for mixing valves or dimming regulation, electric power applied to the fan, etc. 6 Control1H for heating/cooling; Control1V for ventilation; Control1A for artificial lighting; Control1B for blind. 7 Control2H; Control2V; Control2A; Control2B. 5

4

(and possibly some other variables), fa is a function giving the air quality discomfort in function of air change rate Qair and contaminant concentration (and possibly some other variables), and fl is a function giving the visual discomfort in function of solar radiation incidence angle θ and illuminance level El (and possible some other variables).

Host computer weather prediction

user prediction

room prediction

Control3 Optimisation

inputs from host

par2A par2V par2B par2H

general measured variables

outputs to host

heat

network I/O WARP Control2H Control2V Control2A Control2B

I/O

microcontroller Control1H Control1V Control1A Control1B

I / O

vent. light blind

local input variables Figure 4 – Integrated control system for heating/cooling-ventilation-artificial/natural-lighting.

Figure 5 depicts the topology of the hardware concept which allows to implement the software control concept

Sensors

Setpoint device

Actuators LonWorks

Intelligent Room Controller

Host PC Room

Figure 5 – Topology of the hardware integrated control concept.

The integrated software control concept requires different components all connected through a LonWork bus. •

Room setpoint devices: which only outputs variable and enables the user to set both the internal temperature and illuminance; 5

•

Sensors: connected to the LonWorks bus by network nodes output measured variables with a unique identifier for each variable.

•

Actuators: receive and process a high level control information such that they control the subsequent actuator devices.

•

A remote PC host-computer: receives, processes, stores and outputs information, and it is the platform to perform predictive controllers.

•

Intelligent room controller (IRC): which is a unique node of the LonWork bus and it carries out the local room control, receiving local and predictive variables, by means of software modules loaded on Neuron chip, ST6 and WARP2 Fuzzy Logic Processor.

CONCLUSION The integration of intelligent services in building automation industry is still poor and users are not yet convinced of the system reliability because of lack of comprehensive and integrated algorithms, reluctance of industry to develop joint programmes, absence of information about these systems among the practitioners, poor national legislative norms and standards pushing on energy savings in buildings. The expected benefits of the EDIFICIO project from the integrated control strategies, based on SCT, will be demonstrated and quantified in terms of energy saving, by increasing efficiency of BEMS, and indoor comfort, by combining the control actions with human behaviour. It is also expected to introduce innovation in building energy management and a new methodology for industries willing to adapt predictive controls for the overall management of the building services.

ACKNOWLEDGEMENT Authors acknowledge the EU Commission for co-funding the EDIFICIO project.

REFERENCES [1] "A smart control strategy for shading devices to improve the thermal and visual comfort". A. Galatà, F. Proietto Batturi, R. Viadana. 4th European Conference: SolarEnergy and Urban Planning, Berlin 1996. [2] "VESCO - Verifiche su sistemi innovativi per il controllo Energetico-Ambientale degli Edifici". Risparmio Energetico nella climatizzazione degli Edifici. Ordine Quadro ENEL-CONPHOEBUS 1995-1996. Edificio ENEL D.E.R. di Bologna. [3] "DELTA, A Blind Controller Using Fuzzy Logic", LESO-PB/EPFL (CH), November 1996. [4] "Neuronaler Regler in der Klimatechnick", J.Krauss, M. Bauer, M. El-Khoury, Swissbau ’98, InfrastructaKongress, Basel, Jan 98.

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