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Causal thinking emphasizes the understanding of asymmetric causal relationships between variables, requiring us to specify which variable is the cause (independent variable) and which is the effect (dependent variable). Reversing the causal relationship direction can lead to profoundly different assumptions and interpretations. We demonstrate this by comparing two linear regression approaches used in thermal comfort research: Approach (a), which regresses thermal sensation votes (y-axis) on indoor temperature (x-axis); Approach (b), which does the reverse, regressing indoor temperature (y-axis) on thermal sensation votes (x-axis). From a correlational perspective, they may appear interchangeable, but causal thinking reveals substantial and practical differences between them. Approach (a) represents occupants’ thermal sensations as responses to indoor temperature. In contrast, Approach (b), rooted in adaptive comfort theory, suggests that thermal sensations can trigger behavioral changes, which in turn alter indoor temperature. Using the same data, we found that two approaches lead to different neutral temperatures and comfort zones. Approach (b) leads to what we call a ‘preferred zone’, which is 10 °C narrower than the conventionally derived comfort zone using Approach (a). We hypothesize that the ‘preferred zone’ might be interpreted as thermal conditions that occupants are likely to choose when they have significant control over their personal and environmental thermal settings. This finding has important implications for occupant comfort and building energy efficiency. We highlight the importance of integrating causal thinking into correlation-based statistical methods, which have been prevalent in building science research, especially given the increasing volume of data in the built environment.

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability.

Building performance simulation is an important tool in building design and operations. Its purpose is to evaluate and optimize energy use, environmental impact, and occupant comfort of buildings. However, the current state of building performance simulation tools is highly fragmented, and the models themselves can be of low quality. In this paper, we present a platform-based design paradigm for building performance models. This approach offers a standardized design flow to ensure that the models are developed in a consistent and systematic way. Additionally, our approach addresses the lack of model performance metrics, allowing for the quantification of model performance. We explore the design flow and model performance quantification with a case study, demonstrating the use of the platform-based design paradigm.

Most large commercial buildings have digital controls for their heating, ventilation, and air-conditioning (HVAC) and lighting systems with the potential to implement advanced control strategies and data analytics. However, advanced control strategies and data analytics are rarely deployed at scale due to non-standard naming conventions and heterogenous building configurations. Semantic metadata standards, like Brick, show promise to proliferate these applications across many buildings, but they have not been widely adopted by industry due to barriers such as perceived risk and unfamiliarity with the technology. This paper describes the workflow we established and evaluated while using it to develop over ten Brick models of existing buildings. Through this process, we observed that digitizing existing commercial buildings is a cost and labor-intensive effort in which understanding the buildings’ data streams is the major bottleneck. Yet, we conclude this investment is worthwhile since various use case applications such as fault detection and diagnostics, thermal comfort analysis, and HVAC control optimization can utilize the same Brick model. The paper also explores the challenges and lessons learned we encountered while creating these data models, such as: 1) difficulties in finding metadata descriptions and relationships for existing buildings; 2) handling missing concepts in the schema needed to model a building; 3) lack of guidance on how to structure the data model or how much detail to include; 4) unfamiliarity with technologies, which makes the learning curve steep for applications developers. Finally, we also describe future directions for semantic metadata research and development to make such transformative technologies more accessible to practitioners.

Thermal comfort in buildings is typically assessed through occupant surveys, especially for short-term thermal comfort. For long-term thermal comfort, thermal comfort standards and recent research suggest continuous physical monitoring of temperature is sufficient. However, a lack of formal rules for data representation in building automation systems and the high costs of analytical application development for buildings impede predicting long-term thermal comfort at scale. This paper demonstrates portable and reproducible application development techniques for evaluating long-term thermal comfort with the Brick metadata schema and Mortar data testbed. We take advantage of the relatively large Mortar dataset containing over 25 buildings to improve the generalizability of long-term thermal comfort evaluation. Previous research often performs analysis on limited datasets.The design of Mortar enables running the same software applications across many heterogeneous buildings, simplifying building analytics application development, and acting as a vehicle for reproducible evaluations in building science. To assess the efficacy of this workflow, we identify six air temperature- based long-term thermal comfort evaluation metrics from the literature and implement them in software. The six indices are temperature mean index, temperature variance index, degree hours index, range outlier index, daily range outlier index, and combined outlier index. During the application development, we find that the calculation of threshold in the daily range outlier index is arbitrary, and the months belonging to cooling and heating seasons with different comfortable temperature ranges are unclear. Also, all long-term thermal comfort indices fail to differentiate between tool hot and too cold. To address this, we develop two new metrics to calculate overheating and overcooling separately. We evaluate our software across all the buildings available in the Mortar testbed. The result shows that 25 buildings with 1953 thermal zones have qualified air temperature sensor data during building occupancy. Based on this building dataset, we analyze Pearson correlation among long-term thermal comfort indices. The range outlier index has a 0.19 Pearson correlation coefficient with the daily range outlier index, compared with the Pearson correlation coefficient of-0.35 at a randomly selected building in Mortar. The opposite result indicates that a small building dataset is not capable of long-term thermal comfort indices development, generating misleading results. With the help of the uniform Brick metadata schema, we also investigate disaggregating the results by buildings, floors, zones, and equipment. We summarize them as a means of identifying problem areas and equipment.

Green Building XML (gbXML) is one of the most prevalent data models used for Building Information Modeling (BIM). It enables interoperability between BIM and Building Energy Modeling (BEM). BEM is often used in building performance analysis software tools. However, through interviewing engineers and building energy modeling professionals in the industry, it turned out that most gbXML files are only used for importing and exporting building geometry information. Information such as Heating, Ventilation, and Ait-conditioning (HVAC) systems and internal loads are rarely handled due to the lack of functionality in current BIM software. Taking account of more than 15% of the total energy consumption in the US is used by HVAC systems (DOE 2011), it is crucial to enable seamless HVAC data exchange between BIM software and BEM software. This paper researched the definition rules of HVAC systems in the current gbXML schema 6.01 version. Firstly, through a detailed data mapping of ASHRAE baseline HVAC system from IDF data model (EnergyPlus version 9.0) to current gbXML schema (version 6.01), interoperability issues were discovered and were concluded into three catecgories: missing components, overlapping components, and complex data mapping rules. Secondly, through redefining data mapping rules of current gbXML schema in terms of HVAC systems, the ASHRAE baseline HVAC system is able to be coded as a gbXML file. Finally, the gbXML file will be validated through a translation case study, focusing on primary HVAC system and boiler data. Revit 2020.1 Architecture is used in this study as the BIM tool. OpenStudio 2.8.1 and EnergyPlus 9.1.0 are used as the BEM tools. They are opensource and cross-platform, being adopted by lots of mainstream building performance analysis software. Based on the result of this study, current gbXML schema version 6.01 is capable of defining HVAC systems data, but the data mapping rules need to be documented and presented. Missing components and overlapping components issue could be solved by updating the schema. Interoperability improvement will eliminate the duplicate generation of HVAC data and allows a bidirectional information update between BIM and BEM software tools, supporting a more accurate and efficient building performance analysis process.

Lead-free double-perovskite nanocrystals (NCs) have emerged as a promising material to address the issues of instability and toxicity associated with lead-based materials. Metal ion doping or alloying strategies have been widely used to tailor and optimize the optical and electrical properties of semiconductor materials. In this study, we prepared monodisperse Cs2AgBiBr6 NCs with uniform morphology (8 ± 1 nm) and excellent crystallinity using a thermal injection method. Compared to the photoluminescence quantum yield (PLQY) of Cs2AgBiBr6 NCs (∼0.4%), the highest PLQY of Cs2Ag0.64K0.36BiBr6 NCs reached 6.82%, with an extended exciton lifetime from 2.08 to 10.64 ns, which is approximately 5.1 times longer. The exciton binding energy (Eb) and longitudinal optical phonon energy (ℏωop) of Cs2AgBiBr6 NCs were measured to be 197.9 and 31.7 meV, respectively. For Cs2Ag0.64K0.36BiBr6 NCs, the values of Eb and ℏωop were determined to be 317.6 and 53.1 meV, respectively. These results indicate that the nonradiative exciton recombination in Cs2Ag0.64K0.36BiBr6 NCs is weaker compared to that in Cs2AgBiBr6 NCs, resulting in the improved PLQY. The K+-alloyed Cs2AgBiBr6 NCs exhibit promising potential for application in light-emitting diodes. [ABSTRACT FROM AUTHOR]