Sensors in offices mainly measure environmental data, missing qualitative insights into office workers’ perceptions. This opens the opportunity for active individual participation in data collection. To promote reflection on office well-being while overcoming experience sampling challenges in terms of privacy, notification, and display overload, and in-the-moment data collection, we developed Click-IO. Click-IO is a tangible, privacy-sensitive, mobile experience sampling tool that collects contextual information. We evaluated Click-IO for 20-days. The system enabled real-time reflections for office workers, promoting self-awareness of their environment and well-being. Its non-digital design ensured privacy-sensitive feedback collection, while its mobility facilitated in-the-moment feedback. Based on our findings, we identify design recommendations for the evelopment of mobile experience sampling tools. Moreover, the integration of contextual data with environmental sensor data presented a more comprehensive understanding of individuals’ experiences. This research contributes to the development of experience sampling tools and sensor integration for understanding office well-being.
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This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.
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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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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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Purpose:The International Commission on Illumination (CIE) recommends researchers to investigate a widevariety of behavioural and health outcomes. However, researchers often investigate only a part of occupationalhealth (OH) in relation to light. A literature study (2002–2017) regarding the relationship between office lightingconditions and OH was performed to identify gaps and methodological issues.Method:The OH outcomes investigated in this paper were grouped according to the International Classificationof Diseases and analysed per category: physical and physiological health, mental health, eye health, sleep param-eters and visual comfort.Results:Findings from the literature study (20 eligible papers) showed that all OH aspects were mostly but notexclusively measured subjectively. Furthermore, most studies investigated only a fraction of office lighting par-ameters and OH aspects.Conclusions:It seems that Correlated Colour Temperature (CCT) and illuminance mainly correlate with OH.However, this may also be explained by gaps and methodological issues in studies described in eligible papers.Based on the literature study, an overview was composed elucidating gaps and methodological issues of officelighting and OH studies. It can be used to design and target the purpose of light and health research.
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Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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B4B is a multi-year, multi-stakeholder project focused on developing methods to harness big data from smart meters, building management systems and the Internet of Things devices, to reduce energy consumption, increase comfort, respond flexibly to user behaviour and local energy supply and demand, and save on installation maintenance costs. This will be done through the development of faster and more efficient Machine Learning and Artificial Intelligence models and algorithms. The project is geared to existing utility buildings such as commercial and institutional buildings.
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Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
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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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Purpose: This field study analyses the quality of the actual thermal comfort and indoor air quality in Dutch office buildings. A linear regression analysis was used to determine how much these variables and demographic variables influenced the perceived thermal comfort of office workers.Approach: Data were collected on-spot at two Dutch office buildings (n=182) during the winter of 2015 and showed that the indoor air in these offices is of good quality and did not affect the perceived thermal comfort significantly. The indoor temperature was the most important variable that influenced the perceived thermal comfort. Indoor temperatures ranged in this study from 18 till 24oC and were therefore at the edge of acceptable European and Dutch standards NPR-CR 1752 (1999), NEN-EN 15251(2007) and NEN-EN-ISO 7730 (2007).Findings: Office workers which experienced a indoor temperature of 20oC graded this temperature the highest (6.7 on a scale from 1-10). At 20oC the percentage of workers that was dissatisfied was the lowest (30%). This study also showed that female workers were more likely to have the sensation that is was too cold than male workers. European and Dutch standards prescribe that an indoor temperature between 21 and 23oC should be the most ideal temperature during wintertime. This study indicates that an indoor temperature higher than 22oC might be too warm for office workers in The Netherlands during wintertime and that application might influence office workers’ satisfaction negatively.
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