Buildings contribute to global energy consumption and greenhouse gas emissions, making energy-efficient interventions important for sustainable development. In practice, the design and evaluation of such interventions commonly rely on correlation-based predictive models, which describe statistical associations but provide limited insight into the causal mechanisms linking environmental changes, occupant perceptions, and behavioral responses. As a result, interventions may produce outcomes that differ from expectations. This study introduces the Occupant-Centric Building Intervention Framework (OCBIF) designed to assess effectiveness of building energy interventions related to OBI. It establishes causal relationships among environments, occupant characteristics and perceptions, and adaptive actions by adopting the Driver–Need–Action–System (DNAS) concept. It employs Immersive Virtual Environments (IVEs) to simulate building contexts to allow observations related to occupant-building interaction (OBI) for final validation of building interventions. The case study is demonstrated using scenarios related to building interventions aiming to reduce heater uses. The causal analysis yields insights into thermal comfort and OBI. The results show that thermal sensation mediates the effect of indoor temperature on heater interaction, while age and system accessibility are additional causal influences on OBI. This causal structure explains why changes in indoor temperature do not translate directly into behavioral responses and why identical thermal interventions can lead to heterogeneous outcomes across occupants. Findings revealed that OCBIF bridged gaps in building intervention research providing actionable insights for stakeholders to design interventions enhancing energy efficiency while considering OBI.

Scientific experiments significantly enhance the understanding of human-building interactions in building and engineering research. Recently, conducting virtual reality (VR) experiments has gained acceptance and popularity as an approach to studying human-building interactions. However, little attention has been given to the standardization of the experimentations. Proper standardization can promote the reusability, replicability, and repeatability of VR experiments and accelerate the maturity of this emerging experimentation method. Responding to such needs, the authors proposed a virtual human-building interaction experimentation ontology (VHBIEO). It is an ontology at the domain level, extending the ontology of scientific experiments (EXPO) to standardize virtual human-building interaction experimentation. It was developed based on state-of-the-art ontology development approaches. Competency questions (CQs) were used to derive requirements and regulate the development. Semantic Web technologies were applied to make VHBIEO machine-readable, accessible, and processable. VHBIEO incorporates an application view (APV) to support the inclusion of unique information for particular applications. The authors performed taxonomy evaluations to assess the consistency, completeness, and redundancy, affirming no occurrence of errors in its structure. Application evaluations were applied for investigating its ability to standardize and support generating of machine-readable, accessible, and processable information. Application evaluations also verified the capability of APV to support the inclusion of unique information.

This paper proposes a visual analytics framework that addresses the complex user interactions required through a command-line interface to run analyses in distributed data analysis systems. The visual analytics framework facilitates the user to manage access to the distributed servers, incorporate data from the source, run data-driven analysis, monitor the progress, and explore the result using interactive visualizations. We provide a user interface embedded with generalized functionalities and access protocols and integrate it with a distributed analysis system. To demonstrate our proof of concept, we present two use cases from the earth science and Sustainable Human Building Ecosystem research domain.