Journal of Physics: Conference Series Paper • The following article is Open access Exploring the relationship between light and subjective alertness using personal lighting conditions J. van Duijnhoven1, M.P.J. Aarts1, E.R. van den Heuvel2 and H.S.M. Kort3,4 Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 2042, CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era 8-10 September 2021, EPFL Lausanne, Switzerland Citation J. van Duijnhoven et al 2021 J. Phys.: Conf. Ser. 2042 012119 Download Article PDF References Download PDF 29 Total downloads Turn on MathJax Share this article Share this content via email Share on Facebook (opens new window) Share on Twitter (opens new window) Share on Mendeley (opens new window) Hide article information Author e-mails j.v.duijnhoven1@tue.nl Author affiliations 1 Building Lighting Group, Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands 2 Stochastics, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands 3 Research Centre Healthy and Sustainable Living, University of Applied Sciences Utrecht, Utrecht, The Netherlands 4 Building Healthy Environments for Future Users Group, Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands DOI https://doi.org/10.1088/1742-6596/2042/1/012119 Buy this article in print Journal RSS Sign up for new issue notifications Create citation alert Abstract The discovery of the ipRGCs was thought to fully explain the mechanism behind the relationship between light and effects beyond vision such as alertness. However, this relationship turned out to be more complicated. The current paper describes, by using personal lighting conditions in a field study, further exploration of the relationship between light and subjective alertness during daytime. Findings show that this relationship is highly dependent on the individual. Although nearly all dose-response curves between personal lighting conditions and subjective alertness determined in this study turned out to be not significant, the results may be of high importance in the exploration of the exact relationship.
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Light enables us to see and perceive our environment but it also initiates effects beyond vision, such as alertness. Literature describes that at least six factors are relevant for initiating effects beyond vision. The exact relationship between these factors and alertness is not yet fully understood. In the current field study, personal lighting conditions of 62 Dutch office workers (aged 49.7 ± 11.4 years) were continuously measured and simultaneously self-reported activities and locations during the day were gathered via diaries. Each office worker participated 10 working days in spring 2017. Personal lighting conditions were interpreted based on four of the six factors (light quantity, spectrum, timing, and duration of light exposure). Large individual differences were found for the daily luminous exposures, illuminances, correlated colour temperatures, and irradiances measured with the blue sensor area of the dosimeter. The average illuminance (over all participants and all days) over the course of the day peaked three times. The analysis of the duration of light exposure demonstrated that the participants were on average only exposed to an illuminance above 1000 lx for 72 minutes per day. The interpretation of personal lighting conditions based on the four factors provides essential information since all of these factors may be relevant for initiating effects beyond vision. The findings in the current paper give first in-depth insight in the possibilities to interpret personal lighting conditions of office workers.
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Daylight has been associated with multiple health advantages. Some of these claims are associations, hypotheses or beliefs. This review presents an overview of a scientific literature search on the proven effects of daylight exposure on human health. Studies were identified with a search strategy across two main databases. Additionally, a search was performed based on specific health effects. The results are diverse and either physiological or psychological. A rather limited statistically significant and well-documented scientific proof for the association between daylight and its potential health consequences was found. However, the search based on specific health terms made it possible to create a first subdivision of associations with daylight, leading to the first practical implementations for building design.
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Health symptoms may be influenced, supported, or even controlled via a lighting control system which includes personal lighting conditions and personal factors (health characteristics). In order to be effective, this lighting control system requires both continuous information on the lighting and health conditions at the individual level. A new practical method to determine these continuous personal lighting conditions has been developed: location-bound estimations (LBE). This method was validated in the field in two case studies; comparisons were made between the LBE and location-bound measurements (LBM) in case study 1 and between the LBE and person-bound measurements (PBM) in case study 2. Overall, the relative deviation between the LBE and LBM was less than 15%, whereas the relative deviation between the LBE and PBM was 32.9% in the best-case situation. The relative deviation depends on inaccuracies in both methods (i.e., LBE and PBM) and needs further research. Adding more input parameters to the predictive model (LBE) will improve the accuracy of the LBE. The proposed first approach of the LBE is not without limitations; however, it is expected that this practical method will be a pragmatic approach of inserting personal lighting conditions into lighting control systems.
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Daylight has been associated with multiple health advantages. Some of these claims are associations, hypotheses or beliefs. This review presents an overview of a scientific literature search on the proven effects of daylight exposure on human health. Studies were identified with a search strategy across two main databases. Additionally, a search was performed based on specific health effects. The results are diverse and either physiological or psychological. A rather limited statistically significant and well-documented scientific proof for the association between daylight and its potential health consequences was found. However, the search based on specific health terms made it possible to create a first subdivision of associations with daylight, leading to the first practical implementations for building design.
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Long-term care facilities are currently installing dynamic lighting systems with the aim to improve the well-being and behaviour of residents with dementia. The aim of this study was to investigate the implementation of dynamic lighting systems from the perspective of stakeholders and the performance of the technology. Therefore, a questionnaire survey was conducted with the management and care professionals of six care facilities. Moreover, light measurements were conducted in order to describe the exposure of residents to lighting. The results showed that the main reason for purchasing dynamic lighting systems lied in the assumption that the well-being and day/night rhythmicity of residents could be improved. The majority of care professionals were not aware of the reasons why dynamic lighting systems were installed. Despite positive subjective ratings of the dynamic lighting systems, no data were collected by the organizations to evaluate the effectiveness of the lighting. Although the care professionals stated that they did not see any large positive effects of the dynamic lighting systems on the residents and their own work situation, the majority appreciated the dynamic lighting systems more than the old situation. The light values measured in the care facilities did not exceed the minimum threshold values reported in the literature. Therefore, it seems illogical that the dynamic lighting systems installed in the researched care facilities will have any positive health effects.
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Lighting accounts for a significant amount of electrical energy consumption in office buildings, up to 45% of the total consumed. This energy consumption can be reduced by as much as 60% through an occupant-dependent lighting control strategy. With particular focus on open-plan offices, where the application of this strategy is more challenging to apply due to differences in individual occupancy patterns, this paper covers (1) to which extent individual occupancy-based lighting control has been tested, (2) developed, and (3) evaluated. Search terms were defined with use of three categories, namely ‘occupancy patterns’, ‘lighting control strategy’, and ‘office’. Relevant articles were selected by a structured search through key online scientific databases and journals. The 24 studies identified as eligible were evaluated on six criteria: (1) study characteristics, (2) office characteristics, (3) lighting system characteristics, (4) lighting control design, (5) post-occupancy evaluation, and (6) conclusions, and this was used to answer the research questions. It was concluded that the strategy has not been tested yet with field studies in open-plan offices, but that it needs further development before it can be applied in these type of offices. Although lighting currently tends to be controlled at workspace level, many aspects of the strategy can be further developed; there is potential to further increase energy savings on lighting within open-plan office spaces. Individual occupancy-based lighting control requires further validation, focussing on the factors influencing its energy savings, on its cost effectiveness, and on its acceptability for users.
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Research demonstrated a large variety regarding effects of light (e.g. health, performance, or comfort effects). Since human health is related to each individual separately, the lighting conditions around these individuals should be analysed individually as well. This paper provides, based on a literature study, an overview identifying the currently used methodologies for measuring lighting conditions in light effect studies. 22 eligible articles were analysed and this resulted in two overview tables regarding the light measurement methodologies. In 70% of the papers, no measurement details were reported. In addition, light measurements were often averaged over time (in 84% of the papers) or location level (in 32% of the papers) whereas it is recommended to use continuous personal lighting conditions when light effects are being investigated. Conclusions drawn in light effect studies based on personal lighting conditions may be more trusting and valuable to be used as input for an effect-driven lighting control system.
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Public lighting’s primary purpose is nighttime visibility for security and safety. How to meet so many requirements of so many stakeholders? The key to developing a good plan is to relate lighting to functions of public spaces, because street lighting is more than a technical requirement, a security need, or a design element. It can be thought of and utilized in terms of how the type, placement, and wattage affect how a street is perceived and used. With present-day used street lighting systems however, flexibility is expensive, as is maintenance and energy consumption. A new solution is to use LED lighting with a Direct Current power system. Advantages are a decrease in: energy conversions; material use; amount of switch- boxes; components; labour costs and environmental comfort. The overall implementation of LED and DC will result in better control and efficient maintenance due to integrated bidirectional communication. A challenge is the relatively high investment for these new solutions. Another challenge; DC is not a standard yet in rules and regulations. In the paper the transition to direct current public lighting system will be described with all the pros and cons. A new concept of public ownership, to overcome financial challenges will be discussed. M Hulsebosch1, P Willigenburg2 ,J Woudstra2 and B Groenewald3 1CityTec b.v., Alblasserdam, The Netherlands 2The Hague University of Applied Sciences, The Hague, The Netherlands 3Cape Peninsula University of Technology, Cape Town, South Africa 10.1109/ICUE.2014.6904186
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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