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This report presents lessons learned from twenty-five worldwide real-life case studies implementing the integration of daylighting and electric lighting. The case studies were monitored with respect to energy use for lighting, visual performance, non-visual performance, and users’ satisfaction. The monitoring is largely based on field measurements, but it is also complemented with simulations and calculations where needed.

Machine Learning techniques have been recently investigated as an alternative to the use of physical simulations, aiming to improve the response time of daylight and electric lighting performance-predictions. In this study, daylight and electric lighting predictor models are derived from daylighting RADIANCE simulations, aiming to provide visual comfort to office room occupants, with a reduced use of electric lighting. The aim is to integrate an intelligent control scheme, that, implemented on a small embedded 32-bit computer (Raspberry Pi), interfaces a KNX system for a quasi-real-time optimization of the building parameters. The present research constitutes a step towards the broader goal of achieving a unified approach, in which the daylight and electric lighting predictor models would be integrated in a Model Predictive Control. A verification of the ML performance is carried-out by comparing the model predictions to data obtained in monitoring sessions in autumn, winter and spring 2020-2021, resulting in an average MAPE of 19.3%.

A prototype of interactive data visualization is proposed for Building Management Systems (BMS). The aim is to enhance traditional data presentation format such as graphs and tables to support expert users in early open-ended exploration of building data. Based on data recorded over 3 years in a real office building in Switzerland, a case study is conducted involving 12 practicing expert users with engineering background. Both exploratory and qualitative in nature, this evaluation allows to : (1) show the ability of expert users to use advanced visualization tools to derive meaningful conclusions in limited time, (2) demonstrate the relevance of visual analytics results for the building physics field.

Through the use of a data-centric technique for analyzing Building Management System (BMS) data, the article discusses the shortcomings of current building data acquisition for capturing occupants’ behaviour. Applying machine learning to a real 3-years dataset, the potential of unsupervised learning for discriminating between normal daily patterns (motifs) and abnormal ones (discords) for automatic fault detection is outlined, as well as the difficulty to extract from typical BMS data meaningful insights about usage. Finally, the authors propose design guidelines to better monitor, e.g. acquire and analyze, building function together with building usage through digital means, identifying BMS data as a candidate vehicle for this purpose.

The advent of back-lit screens in contemporary offices has profoundly impacted the field of visual comfort. Screen users typically fail to adapt to changing environments and often require an individual light quality. To this end, the new approach of human-building interaction in computer science would consider the context of occupants interacting with lights. Within this framework, the proposed study complements existing studies on reactions to light dimming with a more tightly controlled environment and the task difficulty adapted individually. To evaluate the impact of back-lit screens on participants reaction to light dimming, two sets of participants were assigned either to adaptive or fixed screen back-light. All participants undergo three different naturalistic light dimming speeds typically happening at sunset (0.5–2 lux/s). This study uncovers the potential for physiological measurements to indicate visual discomfort and participants' proclivity to react as a function of personal psychological traits. Importantly, results reveal two behaviour categories: reactive and non-reactive, which are related to participant-specific psychological traits and perceptions. Regardless of the group, eye movements convey early indication of discomfort: participants’ reactions happen below standard light levels (300 lux) while fixation number shift state above. Together, eye-movements features and visual comfort questionnaires recordings during the initial steady light period predict ramp behaviour category. Physiological and psychological measurements thus appear as candidates for adaptive light control according to idiosyncratic characteristics, displaying opportunities for increased acceptance of energy-efficient automation strategies.

The lightness and thermal performances of timber has led designers to consider using it for urban densification and to make it the key for a more sustainable and affordable construction industry.This project of a timber-framed high-rise building will become one of the tallest in Switzerland to adopt a wooden construction, using a mix of two types of manufactured wood: cross-laminated timber (CLT) for structural walls and glue-laminated timber (a.k.a. glulam) combined to an upper concrete layer linked with screws for the slabs. The use of timber sourced from local forest is considered by the engineers because its abundance in Switzerland.The concrete layer is needed to reach a high level of acoustic performance and to efficiently create horizontal diaphragms for earthquake resistance. It also enables the reduction of the thickness of the complex. The lower wooden surfaces with warm natural appearance are visible from the rooms, as well as the vertical surfaces of the CLT wall supporting them.The project reveals the complexity for timber structures to simultaneously comply with regulations concerning structural, fire safety, acoustical and earthquake-resistance performances. Building Information Modeling (BIM) allows excellent technical installations coordination to reach a high degree of prefabrication.

This paper discusses the advantages of using a coding interface both to describe form and run performance simulations in the context of architectural design. It outlines the relevance of combining recent interest in the design community for parametric scripting with available expert-level Building Performance Simulation. This approach enables designers to address performance-related design questions at the early design stage. Pitfalls when considering non-standard solutions and the potential of the approach to circumvent such difficulties are exemplified through a housing building case study, emphasizing the under-evaluated role of the analysis tool in steering design decisions. To circumvent the difficulty for non-expert users to interpret simulation results and transform them into design moves, the use of sensitivity analysis is proposed for its specific interplay with parametric scripting. Its potential for classifying parameters in order of importance is presented in the context of a design problem. The contribution outlines how providing designers with exploratory tools allows to consider sustainable construction in a systemic manner. (C) 2013 Elsevier B.V. All rights reserved.

The paper presents an approach which combines parametric geo metric description techniques performance simulation and sensi tivity analysis to achieve a quantitative analysis of the influence of selected design parameters on performance. By using a scripting environment for parametric design linked to the EnergyPlus build ing simulation tool it becomes possible to automatically screen and simulate thermal behaviour over a selected design parameter space. Then by defining one or several performance metrics their sensitivity to modification of design parameters can be com puted through established statistical techniques for sensitivity anal ysis. This approach provides quantitative insight into the level of in fluence of each parameter under scrutiny informing about changes altering enhancing or having only minor effect on performance. This application of sensitivity analysis to parametric performative design is presented through the help of a case study. Results and implications are discussed respective to their relevance to the early design phase of an architectural project. The framework in defini tive provides the design team with means to balance design inten tions with performance aims.

We describe a new class of decentralised control algorithms that link local wireless connectivity to low-level robot motion control in order to maintain both swarm aggregation and connectivity, which we term “coherence”, in unbounded space. We investigate the potential of first-order and second-order connectivity information to maintain swarm coherence. For the second-order algorithm we show that a single \(\beta \) parameter—the number of shared neighbours that each robot tries to maintain—acts as an “adhesion” parameter. Control of \(\beta \) alone affects the global area coverage of the swarm. We then add a simple beacon sensor to each robot and show that, by creating a \(\beta \) differential between illuminated and occluded robots, the swarm displays emergent global taxis towards the beacon; it also displays interesting global obstacle avoidance properties. The chapter then extends the idea of \(\beta \) heterogeneity within the swarm to demonstrate variants of the algorithm that exhibit emergent concentric or linear segregation of subgroups within the swarm, or—in the presence of an external beacon—the formation of horizontal or vertical axial configurations. This emergent swarm morphology control is remarkable because apparently simple variations generate very different global properties. These emergent properties are interesting both because they appear to have parallels in biology, and because they could have value to a wide range of future applications in swarm robotics.

In the present study, we are interested in verifying how the progressive addition of constraints on communication and localization impact the performance of a swarm of small robots in shape formation tasks. Identified to be of importance in a swarm-user interaction context, the time required to construct a given spatial configuration is considered as a performance metric. The experimental work reported in this paper starts from global and synchronized localization information, shown to be successful both on a real hardware system and in simulation. In a second step, communication is constrained to a local scale, thus obliging a single designated robot to disseminate the global localization information to the other agents. The reliability of the radio communication channel and its impact upon the performance of the system are considered.

Swarm Intelligence provides us with a powerful new paradigm for building fully distributed decentralised systems in which overall system functionality emerges from the interaction of individual agents with each other and with their environment. Such systems are intrinsically highly parallel and can exhibit high levels of robustness and scalability; qualities desirable in high-integrity distributed systems. Making use of a laboratory based swarm robotic system as a case study, this review paper explores dependability, robustness and reliability modelling in swarm based systems, and argues that there is considerable merit in further investigating their application to distributed safety-critical systems.

Initiated by N. Reeves, the Mascarillons project stands at a crossroad between Art and Science. It aims to bring together researchers in both artistic and scientific domains to collaborate towards the production of a robotic environment dedicated to architectural research, with a major potential for multi-media performance. Because of the tight constraints of the project, a multi-level design methodology is proposed, consisting of the parallel development of real robots and increasingly abstract modeling tools. The interaction of these experimental levels is expected to hasten the progress towards an efficient solution by taking every technological and physical constraint into account.

This review paper sets out to explore the question of how future complex engineered systems based upon the swarm intelligence paradigm could be assured for dependability. The paper introduces the new concept of ‘swarm engineering': a fusion of dependable systems engineering and swarm intelligence. The paper reviews the disciplines and processes conventionally employed to assure the dependability of conventional complex (and safety critical) systems in the light of swarm intelligence research and in so doing tries to map processes of analysis, design and test for safety-critical systems against relevant research in swarm intelligence. A case study of a swarm robotic system is used to illustrate this mapping. The paper concludes that while some of the tools needed to assure a swarm for dependability exist, many do not, and hence much work needs to be done before dependable swarms become a reality.

A smart luminaire able to change the direction of its light beam is installed in an office environment and compared to the available LEDs fixtures. Standard lighting measures are performed and a controlled user experiment is conducted, involving 22 subjects. Qualitative results suggest that very low illuminance levels can be characterized as comfortable. Quantitative results supported by statistical significance reveal (1) wider use range of illuminance levels for the smart luminaires, (2) an interaction decrease during the experiment and (3) different usage of the luminaire depending on the subject’s side. Advantages and drawbacks of digitally actuated luminaires are briefly discussed.

Recognized as a powerful methodology for the evaluation of environmental burdens, life cycle assessment (LCA) must be performed with close-to-reality inputs to be robust and accurate. However, the necessary real-world data is hardly available at the design stage, resulting in current LCA practice being mainly based on standards and norms as hypothesis of building contexts, therefore inducing uncertainties in results. The current paper presents a methodology to collect a subset of such input data in a function of the context more accurately than the standards. It then studies the impact of such uncertainties in the LCA results. Through an academic building case study that measured data concerning the building occupancy (A), i.e. the hourly occupancy rate and density, the hourly appliance consumption rate (B), and the hourly conversion factors of environmental impact of the electricity mix (C), the LCA results for the GHG emissions and primary energy consumption are compared between normative- and measurements-based input parameters. The measured occupancy rate (A) is shown to impact the LCA results the most, especially the embedded impacts, by implying a new occupancy density: the building population increase of +32% leads to a significant increase of the embedded impacts related to furniture. The variability in appliance usage (B) is marginal between measures and standards and therefore does not lead to a significant change in LCA results. The use of hourly conversion factors (C) indicates an underestimation of the GHG emissions and, at the same time, an overestimation of the primary energy when assessed with mean annual values. The combined effect of simultaneously using the three reality-based input parameters (A, B, C) mostly affects the non-renewable part of the cumulative energy demand indicator (-9% reduction of the operational part), followed by the cumulative energy demand (-7%) and GHG emissions indicators (-3%). The research findings affect not only LCA research but also practitioners such as architects or building contractors who need to respect ambitious environmental targets.