A review of open loop control strategies for shades, blinds and integrated lighting by use of real-time daylight prediction methods

Jain, Sneha; Garg, Vishal

doi:10.1016/j.buildenv.2018.03.018

Cited by 77 publications

(34 citation statements)

References 65 publications

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“…The authors in Ref. [42] presented an exhaustive survey on open loop control strategies for the control of shades, blinds and integrated lighting systems. The authors show that available methods depend on the an external photo sensor/camera for control strategies.…”

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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“…The predictive model for the blind position (equation (1)) along with daylight estimation model (Figure 4) implemented in the test workbench helps to set the dimming control of the LED luminaires from 8 am to 6 pm based on the control algorithm shown in Table 2. Also, as given in the previously reported studies, 15,16,24,25 from 8 am to 6 pm, daylight estimation helps to evaluate the necessary light output for windows on all four sides. These two conditions help to set the desired illuminance.…”

Section: Discussionmentioning

confidence: 90%

“…In the studies by Jain and Garg 15 and Mahdavi, 16 simulation-based predictive models for the lighting and shading system are discussed. In previous studies, [17][18][19][20] a rulebased control strategy was used to improve comfort and uniformity.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for the control of a daylight–artificial light integrated scheme

Kumar

Kurian

Shetty

2019

Lighting Research & Technology

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This paper presents a data analytics model-based predictive control approach for a daylight–artificial light integrated scheme. The essential data are collected from an automated test room with dimmable LED luminaires and motorized Venetian blinds. This study considered machine learning techniques to develop a novel control strategy for all the four-window orientations for maintaining comfort and energy conservation. The shades are operated one at a time, and the annual data collected were used to develop the predictive models. The irradiance, altitude, temperature and daylight on the window are the predictors, and the blind position is the response variable to establish the models for the windows on all four sides of the test room. The standard support vector regression, Bayesian support vector regression and Gaussian process regression models are analysed in comparison with the baseline model. The luminaire dimming control signals generated using the predicted optimum blind position and exterior illuminance based on a building information illuminance model is commissioned for a given room. This approach mainly concentrated on the implementation of an industrial-level product by reducing the computational complexity of the rule-based blind positioning system. At present, the models are in a reconfigurable embedded WiFi-enabled operating system.

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“…A similar upper threshold was found by Bülow-Hübe [36], who reported an average 2600 lux illuminance on the work plan triggered the closing of either the awning or the venetian blind. In addition to blind's controls based on occupants' usage patterns, other forms of control strategies are possible, such as glare metric and sun profile [37]. In such a perspective, Yun et al determined that a vertical eye illuminance of 3000 lux corresponds to the threshold at which glare occurs [38].…”

Section: Limitations and Discussionmentioning

confidence: 99%

Automatic vs Manual Control Strategy for Window Blinds and Ceiling Lights: Consequences to Perceived Visual and Thermal Discomfort

Lolli

Nocente

Brozovsky

et al. 2019

Journal of Daylighting

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A case study to evaluate the occupants' satisfaction in relation to two different control strategies (fully automatic and manual) for blind and ceiling lights use in cell offices was carried on in Trondheim, Norway. A group of 11 participants with varying age, gender, and ethnicity, used two test cells of a laboratory as a workspace primarily carrying out office tasks at a personal computer for a total of 19 calendar days. The participants were asked to answer a computer-based questionnaire for reporting their perceived thermal and visual comfort. Concurrently, measurements of the indoor operative temperature, illuminance level, and operation of windows, blinds, and ceiling lights were registered. Results shows that the use of the automatic control strategy led to a higher visual discomfort, which in addition led to a higher thermal discomfort, despite this last not caused by a higher average operative temperature.

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A review of open loop control strategies for shades, blinds and integrated lighting by use of real-time daylight prediction methods

Cited by 77 publications

References 65 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

A data-driven approach for the control of a daylight–artificial light integrated scheme

Automatic vs Manual Control Strategy for Window Blinds and Ceiling Lights: Consequences to Perceived Visual and Thermal Discomfort

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