Implementation of Fuzzy Logic in Daylighting Control

Cziker, A.; Chindris, M.; Miron, Anca

doi:10.1109/ines.2007.4283697

Cited by 34 publications

(11 citation statements)

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“…Considering daylight is a dynamic source of lighting, .i.e. the illu minance fro m the sky is variab le, and the variations in daylight can be quite large depending on season, location or latitude, and cloudiness, therefore traditional control systems, based on mathematical models, and often implemented as "proportional-integral-derivative (PID)" controller, have their limits as daylight perception lighting controller [3]. Taking into account the random pattern of potentially available daylight and rapid change of its characteristics, fuzzy control has proved to be a more convenient solution.…”

Section: Figure I Basic Principle Of Daylight Perceptive Lightingmentioning

confidence: 99%

Explore on Illuminance Control of Daylight Perceptive Lighting by Fuzzy Logic

Gao¹,

Guo²,

Lin³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Basic concept of daylight perceptive lighting is that intensity of lamps are adjusted opposite to the trend of sun light intensity, so that constant illuminance on work surface is kept. The classic and compact arrangement of daylight perceptive lighting system were discussed, the compact was recommended from the view point of facilitating construction, although illuminance control by the classic is relative simpler than the compact. Considering visual character of human eyes, accurate illuminance control is not necessary; the key is how to use the character to realize a general policy. Through analysis and test, fuzzy logic is a good choice to illuminance control by the compact scheme. Based on fuzzy logic, elementary explore on illuminance equivalence fuzzifying control variable, was done and the possible membership function was given.

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Section: Figure I Basic Principle Of Daylight Perceptive Lightingmentioning

confidence: 99%

Explore on Illuminance Control of Daylight Perceptive Lighting by Fuzzy Logic

Gao¹,

Guo²,

Lin³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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show abstract

“…Luigi Martirano [11] took advantage of BAS (Building Automation System) and solar energy to validate a fuzzy logic approach to optimize the level of energy performance and comfort in an office space. Based on daylight and lighting of unoccupied spaces, the lighting control plays an important role by reducing the energy wasted in unoccupied hours in the building and automatically adjusts the electrical light level according to daylight based on the façade ( [12,13]). In the above research the weather condition can be a problem for the use of the day light such as-sky covered, the season of the year particularly in winter.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Management Strategy for Automated Office Buildings

2019

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The increasing demand to reduce the high consumption of end-use energy in office buildings framed the objective of this work, which was to design an intelligent system management that could be utilized to minimize office buildings’ energy consumption from the national electricity grid. Heating, Ventilation and Air Conditioning (HVAC) and lighting are the two main consumers of electricity in office buildings. Advanced automation and control systems for buildings and their components have been developed by researchers to achieve low energy consumption in office buildings without considering integrating the load consumed and the Photovoltaic system (PV) input to the controller. This study investigated the use of PV to power the HVAC and lighting equipped with a suitable control strategy to improve energy saving within a building, especially in office buildings where there are reports of high misuse of electricity. The intelligent system was modelled using occupant activities, weather condition changes, load consumed and PV energy changes, as input to the control system of lighting and HVAC. The model was verified and tested using specialized simulation tools (Simulink®) and was subsequently used to investigate the impact of an integrated system on energy consumption, based on three scenarios. In addition, the direct impact on reduced energy cost was also analysed. The first scenario was tested in simulation of four offices building in a civil building in South Africa of a single occupant’s activities, weather conditions, temperature and the simulation resulted in savings of HVAC energy and lighting energy of 13% and 29%, respectively. In the second scenario, the four offices were tested in simulation due to the loads’ management plus temperature and occupancy and it resulted in a saving of 20% of HVAC energy and 29% of lighting electrical energy. The third scenario, which tested integrating PV energy (thus, the approach utilized) with the above-mentioned scenarios, resulted in, respectively, 64% and 73% of HVAC energy and lighting electrical energy saved. This saving was greater than that of the first two scenarios. The results of the system developed demonstrated that the loads’ control and the PV integration combined with the occupancy, weather and temperature control, could lead to a significant saving of energy within office buildings.

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“…Hagras et al (2008) presented used intelligent real time control to minimize building energy use. Cziker et al (2007) used a fuzzy control for illumination control considering the impact of various parameters on the illumination level. Mitsunori et al (2013) presented a Linear Programming method for intelligent lighting systems.…”

Section: Introductionmentioning

confidence: 99%

An optimal model for tunnel lighting control systems

2015

Tunnelling and Underground Space Technology

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Implementation of Fuzzy Logic in Daylighting Control

Cited by 34 publications

References 0 publications

Explore on Illuminance Control of Daylight Perceptive Lighting by Fuzzy Logic

Explore on Illuminance Control of Daylight Perceptive Lighting by Fuzzy Logic

Intelligent Energy Management Strategy for Automated Office Buildings

An optimal model for tunnel lighting control systems

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