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With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment.

In the architecture, engineering, and construction industries, the integration of Building Information Modeling (BIM) has become instrumental in shaping the design and commissioning of smart buildings. At the center of this development is the pursuit of more intelligent, efficient, and sustainable built environments. The emergence of smart buildings equipped with advanced sensor networks and automation systems increasingly requires the implementation of Digital Twins (DT) for the direct coupling of BIM methods for integral building planning, commissioning, and operational monitoring. While simulation tools and methods exist in the design phase of developing advanced controls, their mapping to construction or post-construction models is less well developed. Through systematic, keyword-based literature research on publisher-independent databases, this review paper gives a comprehensive overview of the state of the research on BIM integration of building control systems with a primary focus on combined controls for daylight and artificial lighting systems. The review, supported by a bibliometric literature analysis, highlights major development fields in HVAC controls, failure detection, and fire-detection systems, while the integration of daylight and artificial lighting controls in Digital Twins is still at an early stage of development. In addition to already existing reviews in the context of BIM and Digital planning methods, this review particularly intends to build the necessary knowledge base to further motivate research activities to integrate simulation-based control methods in the BIM planning process and to further close the gap between planning, implementation, and commissioning.

The Daylight Glare Probability (DGP) includes the luminance of a glare source quadratically, but the solid angle only linearly. While this is in line with formulae of other glare metrics, it must be questioned for small glare sources, if the glare stimulus can no longer be distinguished from larger stimuli causing equal vertical illuminance at the eye, especially in the peripheral visual field. To account for this, the modified version Daylight Glare Metric (DGM) was previously developed. We conducted two studies to evaluate the effect of the modified DGM. First, in a laboratory study under an artificial sky with an LED sun, 35 test subjects evaluated different glare situations. Second, we performed a comprehensive simulation study for an office space, including three locations, three view directions, and 17 window systems (electrochromic glazing, fabric shades). The results from the perception study under the artificial sky provide evidence that the adapted DGM is better suited to predict glare from small, bright sources. The results from the simulation study for a realistic office setting show that, compared to the DGP, the DGM reduces glare ratings for many hours of the year, thus underscoring the practical relevance of improving the DGP formula.

In this paper we present some of the challenges faced when upscaling optical microstructures from a lab scale 1x1 cm sized proof of concept sample to a square meter sized object that can be installed in a building. The optical microstructure in question is obtained by a chain of fabrication steps, all linked with each other and with a certain level of complexity. Each of the total of 8 distinct steps presented difficulties that will be briefly introduced in this paper. On a less technical level, long term commitment of public funding and industrial partners was the base for the first upscaling of this complex technologies for pilot production. Taking the best from two worlds: industry and academia has proven effective in the development of such a novel technology.

Indoor glare significantly affects the visual comfort and health of occupants, daylighting simulation can act as an effective method to analyze daylight glare issues during building design. To address the extensive computational costs associated with calculating annual daylight glare metrics with existing methods, this study introduces an acceleration framework, which can be widely applied. The framework integrates a newly developed daylight matrix multiplication program ( DMM4GPU ) for Graphics Processing Unit (GPU) computation acceleration and the previously developed Accelerad program, which can accelerate the calculation of Daylight Coefficient (DC) matrices in the Two-phase Method (2-PM), the View matrices in the Three-phase Method (3-PM) and Five-phase Method (5-PM). By comparing with standard Radiance Central Processing Unit (CPU) calculations, the study validated the acceleration framework’s simulation accuracy and significantly reduced computation time in daylight glare metrics calculations. It also analyzed the impact of various simulation parameters on the framework’s performance. Results indicate that the acceleration framework’s error in calculating Daylight Glare Probability (DGP) is minimal (RMSE < 0.004), and the computation times for the 2-PM, 3-PM, and 5-PM are reduced by 94.8%, 93.9%, and 83.0%, respectively. Furthermore, this study discussed modeling techniques to avoid possible errors in the daylight GPU computations.