An artificial intelligence approach to the prediction of natural lighting levels

Coley, David; Crabb, J.A.

doi:10.1016/s0360-1323(96)00043-1

Cited by 22 publications

(4 citation statements)

References 9 publications

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“…The use of artificial intelligence for lighting prediction and lighting control in an office space is at least two decades old [8]. The simplicity, versatility and ability to adapt to the changes have aided such systems to remain useful.…”

Section: Related Workmentioning

confidence: 99%

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Kandasamy

Karunagaran

Spanos

et al. 2018

Building and Environment

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Lighting contributes a significant portion to the overall energy consumption in an office building. It is thus important to reduce the energy consumption of lighting systems especially for Net Zero Energy Buildings (NZEB). Maximizing daylight harvesting can significantly increase the energy savings. With increase in demand for satisfying occupant preferences in visual comfort, the need for personalized lighting in the office space is also rising. In this paper, a novel lighting control system for Net Zero Energy Buildings (NZEB) is proposed which models the lighting system using Artificial Neural Network (ANN) and utilizes this model with the Internal Model Control (IMC) principle for controller design. Modeling the lighting system using ANN reduces the challenge of modeling a large and complex system with inherent process variability without the need to analyze extensive data-sets. The proposed ANN-IMC controller uses feedback from sensors on the task table to maintain desired illuminance, is easy to tune with just one parameter and is robust to process variability. The proposed control design is applicable to square systems where the number of lights and number of sensors are equal. However, the proposed architecture can also be extended for controlling other lighting accessories such as roller blinds. The performance of the proposed lighting control system to harvest the daylight effectively is demonstrated using both simulation results and an experimental setup in test-bed environment. The versatility of the proposed system will allow an operator to deploy personalized lighting in an office space.

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Section: Related Workmentioning

confidence: 99%

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Kandasamy

Karunagaran

Spanos

et al. 2018

Building and Environment

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“…ANN-based methods have also been tested for real-time feedback, specifically for automated blind and lighting control. Coley and Crabb (1997) proposed the application of ANNs to remotely control electric lighting by predicting indoor illuminances at chosen points based on external measurements of vertical illuminance.…”

Section: Application Of Anns In Daylightingmentioning

confidence: 99%

Artificial Neural Networks for parametric daylight design

Lorenz

Spaeth

Souza

et al. 2019

Architectural Science Review

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“…By using the triangle serial membership function to describe the value of fuzzy set, the variances can be minimized. The minimum inference engine application of DSP could be used for fuzzy inference engine [10,11] .…”

Section: Experimental: System Designmentioning

confidence: 99%

Fuzzy Logic Control of Photovoltaic Lead-Acid Charging System

Yarn

Wu²,

Chung

2011

AMR

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A new photovoltaic solar cell charging system with fuzzy logic control is proposed and designed. This kind of solar energy storage system is composed of a solar cell, a charger, batteries, a buck converter and a digital signal processor. It mainly applies a low-voltage level translator to control the pulse charging current of a lead-acid battery and also combines the fuzzy control method to improve the charging efficiency, suppress the abnormal temperature rise in the battery, lengthen the battery life-time, reduce the additional waste, etc..Experimental and simulated results are shown to demonstrate and compare the validity of the system and verify the effectiveness with each other.

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An artificial intelligence approach to the prediction of natural lighting levels

Cited by 22 publications

References 9 publications

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Artificial Neural Networks for parametric daylight design

Fuzzy Logic Control of Photovoltaic Lead-Acid Charging System

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