Occupant centered lighting control: A user study on balancing comfort, acceptance, and energy consumption

Nagy, Zoltán; Yong, Fah Yik; Schlueter, Arno

doi:10.1016/j.enbuild.2016.05.075

Cited by 85 publications

(54 citation statements)

References 24 publications

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“…Similar results were found in relation to the control strategy of artificial lighting use. A study by Nagy et al [29] surveyed the occupants of an office building in Zurich, Switzerland, in relation to the use of a dedicated control strategy for artificial lighting. Most of the respondents (80%) agreed on the importance of having control over the lighting environment, and to be able to override the automatic control system (90%).…”

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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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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show abstract

“…For the PMV estimation in our simulation model, constant values of metabolic rate (108 W/person corresponding to light-office activities), clothing level (0.5 clo) and in-space air velocity (0.137 m/s) were considered. Note here that, instead of using the Fanger index, any personalised thermal comfort model [35][36][37][38][39] can be seamlessly integrated in the overall methodology; the only change will be in the thermal discomfort calculation methodology.…”

Section: The Example Buildingmentioning

confidence: 99%

“…In addition, as analysed in [2,25], there is a cost (e.g., express as productivity loss [26,27]) associated with the inability of most MPC-oriented bi-linear models to accurately estimate occupant thermal sensation, e.g., under the guidelines of ISO 7730 [28] or ASHRAE 55 [29], as most MPC applications in buildings define room thermal comfort as a pre-defined region of air [30] or operative [3] temperatures with only few exceptions [31][32][33][34]. This limitation becomes more restricting as personalised thermal comfort models [35][36][37][38][39] have increasingly started becoming the norm. In an effort to overcome these limitations, we propose a methodology that utilises Building Energy Performance (BEP) simulation models, developed using BEP simulation engines such as EnergyPlus [40] or Modelica [41] in place of the purpose-built models for MPC.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

et al. 2018

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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.

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“…The occupant centered control system (OCC) has been introduced to address this discrepancy between building control and occupant comfort (Nagy et al 2015;Nagy et al 2016). There are two components of occupant comfort, one of which is the actual physical comfort, i.e., the absence of the feeling of pain, and the other is the perception of control over their indoor environment (Nagy et al 2016). To accomplish the two parts of comfort, the building control system should be focused, or centered, on the characteristics of the occupant.…”

Section: Introductionmentioning

confidence: 99%

“…An ideal system should learn the unique preferences of the occupant, and adapt the control set-points to these preferences to balance the decisions of the occupant with the calculated decision for energy savings. There are several experimental results indicating that OCC approach can save energy without sacrificing occupant comfort (Nagy et al 2015;Nagy et al 2016).…”

Section: Introductionmentioning

confidence: 99%

LightLearn: Occupant centered lighting controller using reinforcement learning to adapt systems to humans

Park¹,

Dougherty²,

Fritz³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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Humans spend up to 90% of their time indoors, thus building systems should maintain the indoor environment within the comfort range. In this paper, we present LightLearn, a reinforcement learning based occupant centered lighting controller. The control agent interacts with the occupant non-intrusively, learns her/his preferences, and determines actions for achieving both human comfort and energy saving. We present system hardware, control algorithm, and experimental results of LightLearn for an office space. Compared to the full (9am-5pm) and occupancy based control, LightLearn reduced 83% and 63% of operation time, respectively, by adapting to the occupant.

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Occupant centered lighting control: A user study on balancing comfort, acceptance, and energy consumption

Cited by 85 publications

References 24 publications

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

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

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

LightLearn: Occupant centered lighting controller using reinforcement learning to adapt systems to humans

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