Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings


Supervisor of Digital PI-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings


K. Alexandridis and A. I. Dounis


Department of Automation, Technological Educational Institute of Piraeus, Piraeus, P. Ralli & Thivon 250, Greece, Tel: 2105381338, email: aidounis Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings@otenet.gr.


Abstract

In this paper, we develop a supervisor of digital PI-like fuzzy logic controllers (FLC) for indoor lighting conditions control in buildings. The proposed fuzzy control system has Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings hierarchical structure. This structure consists from one supervisor and two fuzzy logic controllers. The supervisor evaluates the daylight and artificial lighting and decides by logic-based switching for the fuzzy controllers’ operation Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings. The structure of a PI-like fuzzy logic controller is presented.

The control system is implemented in a simulation environment including reference models for the building. The environment combines TRNSYS (Transient System Simulation Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings Program) and MATLAB software’s. Τhe role of the real system is played by a model implemented in TRNSYS. The control system is implemented in MATLAB. The communication between TRNSYS and MATLAB is realized Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings by a TRNSYS TYPE that calling the MATLAB Engine Library.

The simulation results show that the proposed fuzzy control system successfully manages the illuminance comfort and the energy conservation.


Keywords Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings: Digital Fuzzy Logic Controller, Energy Saving, Lighting comfort, Building, Supervisor.


1. Introduction


The problem of energy saving and the achievement of visual comfort conditions in the interior environment of a building is multidimensional. Scientists from a Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings variety of fields have been working on it for quite a few decades, but it still remains an open problem. People spend about 80% of their lives inside buildings. So, achieving lighting Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings comfort conditions in a building is very important and has direct implication to the productivity of the occupants and indirect implication to the energy efficiency of the building. Indoor lighting Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings in buildings is a topic of a major importance for researchers.

Dounis [4] proposed a fuzzy control scheme for visual comfort in a building zone. The indoor illuminance levels together with the Daylight Glare Index are Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings taken into account by the fuzzy control scheme to regulate the shading and electric lighting [7].

User behaviours concerning the blind position are often very complex and hardly predictable. Guillemin and Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings Moltemi [6] used Genetic Algorithms in a shading-device controller with goal to learn the user preferences. Guillemin and Morel [5] presented a self-adaptive multi-controller system. In this system every controller Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings works in order to help the others. The overall optimization of the system realised through the use of GA.

In the Lah’s paper [9,10] proposed a modern approach to control the inside Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings illuminance with fully automated fuzzy system for adjusting shades, which responds constantly to the changes in the available solar radiation, which makes decisions as it follows the human thinking process. The main Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings fuzzy logic controller is linked with an auxiliary conventional PID controller. The goal of this lower level controller is the control the roll position.

Hybrid systems like ANFIS (Adaptive Neurο-Fuzzy Inference Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings System) have been used for prediction and control of the artificial lighting in buildings, following the variations of the natural lighting [8].


The present paper presented a method supervision control that uses digital PI-like Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings FLC to improve both lighting level and energy efficiency at the same time. The main goal of the proposed supervisory control system is to take full advantage of daylight for Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings inside lighting.


^ 2. Considered System


2.1 Simulation environment (MATLAB-TRNSYS)


This environment combines TRNSYS 16 [16] and MATLAB software (Fig. 1). The building model is implemented in TRNSYS and the control system is implemented in MATLAB. Simulation time Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings step is 6 min. Controllers outputs belong to interval [0,1] and Φi is the maximum power of each actuator. The simulation environment, shown in Figure 1, includes the following components:

1) TRNSYS TYPE 56 module: Multi Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings-zone Building modeling.

2) TRNSYS TYPE 155: The interface between TRNSYS and MATLAB. The controllers are implemented in MATLAB. For the controllers for which an executable program is available, the file data transfer and the call Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings to executable routine are also implemented in MATLAB. The TYPE 155 is a standard TRNSYS routine.

3) TYPE 9 module: This component is used to read the weather data files (TRY is generated by Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings meteorological data from Athens, Greece [1].

4) TYPE 16 module: This component is a radiation processor with smoothing.

5) The calculations relevant to the natural and artificial illumination, the development of the fuzzy controllers and supervisor are implemented Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings in the MATLAB.

All simulations concerned a passive solar building characterized by an important south-facing window glazed area (3m2), area 45 m2, volume 135 m3 and by a high thermal inertia Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings, light transmittance of the window glazing mean (τ=0.817), reflectance of all indoor surfaces (ρ=0.4). In the TRNSYS there exist an electric lighting (10 lamps, 0-1000 lux, 800 W total), and a shading device (curtain). The controller Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings’s initial set point is: indoor Illuminance= {800-600-500-800}lux. is the maximum power of each actuator.





Figure 1: Simulation block diagram.

2.2 Lighting


Indoor Natural Lighting


The average indoor illuminance Εin (lx) [11] is calculated using the equation




(1)

where

Aw (m2) the Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings window surface

τ (-) the light transmittance of the window glazing

Εv (lx) the vertical illuminance on the window

Ain (m2) the total area of all indoor surfaces

ρ (-) the area weighted mean reflectance of Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings all indoor surfaces.

The vertical illuminance on the window Εv (lx) is given by the following equation



(2)

with

kG (lm.W-1) the luminous efficacy of global solar radiation

Gv (W.m-2) the global solar radiation Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings on the window surface


The luminous efficacy of global solar radiation [13] can be calculated by the following relation




(3)

with

Dh (W.m-2) the diffuse horizontal solar radiation

Gh (W.m-2) the global horizontal solar radiation

kD Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings (lm.W-1) the luminous efficacy of diffuse solar radiation

kS (lm.W-1) the luminous efficacy of beam solar radiation.


The luminous efficacy of diffuse solar radiation [12] is calculated using the equation



(4)






(5)







(6)

with θz (deg Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings) the solar zenith angle.


Finally, the luminous efficacy of the beam solar radiation [2] can be calculated using the relation


kS = 17.72 + 4.4585 θz – 8.7563 10-2 (θz)2 + 7.3948  10-4 (θz)3 – 2.167  10-6 (θz)4 – 8.4132  10-10 (θz)5

(7)


A qualitative criterion for Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings the control performance is the value of Illiminance Discomfort Index (IDI) (Equation 8). ^ K is the index of the sample, Ti the sampling time and ei the sample error.






(8)


Artificial Lighting

The Equation below is used Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings to calculate the average artificial light intensity inside the buildings:




(9)

where

: The actuating signal of the artificial light controller, ranging from 0-1. This signal is driven by the artificial lighting fuzzy controller Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings. The same signal is also fed into the building model (Archimed.bui) to drive the actuator for the artificial lights. If means that all lights are off. If means that all lights are on at Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings full power. In the latter case, the equivalent intensity is approximately EAL=1000 lux. N: Number of light lamps (N=10), P: The power per lamb (P=60W), V: The luminous efficacy Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings/efficiency of each lamp (V=60 lumen/W), n: The power efficiency of each lamp (n=0.7), H: The height lamp from floor (H=3m), h: The height reference working level, measured from the Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings floor (h=1m).


^ 3. Digital PI-like FLC

The proposed PI-like FLC is useful because in the building control systems there are actuators with continuous output such as variable speed fans, hot Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings water heating systems, electrical heaters, air inlets. All the membership functions of the PI-like FLC inputs/outputs are shown in Figures 3 and 4. The input/output normalization maps the state variables on the Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings interval [-1,+1]. The scaling factors are chosen to be =1/1200, =1/120000 and =1. These scaling factors have been found via simulations (trial and error). The output of each controller is The fuzzy control rules Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings are presented in Table 1 and 2.



Figure 2: Digital implementation of a PI-like FLC





Figure 3: Membership functions of the FLCs output variables.




Figure 4: Membership functions of the FLCs input variables.





Δe

e

NB

NM

NS

ZE

PS

PM

PB

NB

PB

PB

PB

PB

PM

PS

ZE

NM

PB

PB

PB

PM

PS

ZE

NS

NS

PB

PB

PM

PS

ZE

NS

NM

ZE

PB

PM

PS

ZE

NS

NM

NB

PS

PM

PS

ZE

NS

NM

NB

NB

PM

PS

ZE

NS

NM

NB

NB

NB

PB

ZE

NS

NM

NB

NB

NB

NB

^ Table 1: The control rules of Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings the fuzzy controller of the shading (ΔuSH)

Δe

e

NB

NM

NS

ZE

PS

PM

PB

NB

NB

NB

NB

NB

NM

NS

ZE

NM

NB

NB

NB

NM

NS

ZE

PS

NS

NB

NB

NM

NS

ZE

PS

PM

ZE

NB

NM

NS

ZE

PS

PM

PB

PS

NM

NS

ZE

PS

PM

PB

PB

PM

NS

ZE

PS

PM

PB

PB

PB

PB

ZE

PS

PM

PB

PB

PB

PB

Table 2: The control rules of the fuzzy controller of the artificial lighting (ΔuAL)


^ 4. Supervisor Architecture

The proposed control system can be referred to as intelligent control Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings system because the actions of the controller attempt to mimic high level decision making processes of human operators. The architecture of supervisor unit is shown in Figure 5 and the supervisor logic is Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings presented in Table 3.




Table 3: Supervisor (logic-based switching)




Figure 5: The architecture of proposed control system

^ 5. Simulation Results

The performance of the two controllers is summarized in Table 2. The performance criteria are the response performance Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings, the illuminance discomfort index, the natural lighting exploitation and the energy consumption for electric lighting. The energy consumption is calculated for the one day simulation period. In Figures 6 and 7 give the response Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings performance of indoor illuminance with and without complete exploitation of natural lighting. The response of system output successfully approaches the set points. In the case 1 the control system involves important energy saving about Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings 90% concerning case 2. However, in the case 2 the control system does not achieve a low IDI since the daylighting is one of the main reason’s that cause glare and Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings visual discomfort in occupants.




Figure 6: Response performance of Indoor illuminance without the complete exploitation of natural lightting (15η April).




Figure 7: Response performance of Indoor illuminance under the complete exploitation of natural lightting (15η April).





Performance of the Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings zone level controllers (Illuminace tolerance=50lux)

Performance without the complete exploitation of natural lighting (Case 1)

Performance under the complete exploitation of natural lighting (Case 2)

IDI=21.865 lux

IDI=27.570 lux

Natural lighting exploitation=56%

Natural lighting Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings exploitation =94%

Response Performance

Overshooting: approximately zero

Steady state error: approximately zero

Response Performance

Overshooting: about 32%

Steady state error: approximately zero

Energy consumption (KWh/m2)


Artificial lighting=31 10-3

Motor for shading=1,26610-6

Energy consumption (KWh/m2)


Artificial Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings lighting=2,9 10-3

Motor for shading=2,05510-6

^ Table 4: The performance without and with without the complete exploitation of natural lighting

6. Conclusions

In this paper, we develop a supervisor of digital PI-like fuzzy logic controllers for indoor lighting conditions control Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings in buildings. The simulation results show that the proposed fuzzy control system is achieved illuminance comfort and important energy savings. The supervisory control system achieves energy saving based on the Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings full exploitation of the daylight.

Acknowledgements

The project is co-funded by the European Social Fund & National Resources - EPEAEK II – ARCHIMIDIS.


References


[1] Argiriou, S. Lykoudis, S. Kontoyiannidis, C. A. Balaras, D. Asimakopoulos, M Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings. Petrakis and P. Kassomenos, “Comparison of Methodologies for TMY Generation Using 20 Years Data for Athens, Greece”, Solar Energy, vol. 66, no. 1, pp. 33-45, 1999.

[2] S. Aydinli and J. Krochmann, “Data on daylight and solar radiation: Guide Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings on Daylight”, Draft for CIE TC 4.2, 1983.

[3] ASHRAE, Handbook – Fundamentals, 2005.

[4] A. I. Dounis, M. J. Santamouris, C. C. Lefas, “Building Visual Comfort Control with Fuzzy Reasoning”, Journal of Intelligent and Fuzzy Systems, vol. 34, no. 1, pp Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings.17–28, 1993.

[5] A. Guillemin and N. Morel, “An innovative lighting controller integrated in a self-adaptive building control system”, Energy and Buildings, vol. 33, no. 5, pp. 477–87, 2001.

[6] A. Guillemin, S. Molteni, “An energy-efficient controller Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings for shading devices self- adapting to the user wishes”, Building and Environment, vol. 37, pp.1091–1097, 2002.

[7] D. Kolokotsa, “Comparison of the performance of fuzzy controllers for the management of the indoor environment”, Building Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings and Environment, vol. 38, no. 12, pp. 1439–1450, 2003.

[8] C. P. Kurian, S. Kuriachan, J. Bhat, R. S. Aithal, “An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes”, Lighting Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings Res. Technoogy,. vol 37, no. 4, pp. 343-352, 2005.

[9] Lah MT, Borut Z, Krainer A., “Fuzzy control for the illumination and temperature comfort in a test chamber”, Building and Environment, vol. 40, pp. 1626–1637. 2005.

[10 Lah MT, Borut Z, Peternelj J Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings, Krainer A. Daylight illuminance control with fuzzy logic. Solar Energy 2006;80:307-321.

[11] DHW Li and JC Lam, “Measurements of solar radiation and illuminance on vertical surfaces and daylighting implications”, Renewable Energy Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings, vol. 20, pp. 389-404, 2000.

[12] P. Littlefair, S. Ashton and H. Porter, "Luminous efficacy algorithms”, Joule 1 Program – Dynamic characteristics of daylight data and daylighting design in Buildings, Final Report, CEC Brussels, 1993.

[13] M. Perraudeau, “Estimation of Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings illuminances from solar radiation data”, Joule 2 DAYLIGHT II Program: Availability of Daylight – Design of a European Daylighting Atlas, CSTB Nantes, 1994.

[14] TRNSYS 16: A Transient System Simulation Program, Users manual, Solar Energy Laboratory, University Supervisor of Digital pi-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings of Wisconsin-Madison, (2006).
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