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1. Brick: Towards a Unified Metadata Schema For Buildings

   https://doi.org/10.1145/2993422.2993577  

   Balaji, Bharathan; Agarwal, Yuvraj; Berges, Mario; Culler, David; Gupta, Rajesh; Kjærgaard, Mikkel Baun; Srivastava, Mani; Whitehouse, Kamin; Bhattacharya, Arka; Fierro, Gabriel; et al (November 2016, BuildSys '16 Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments)

   Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings. 

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2. Plaster: an integration, benchmark, and development framework for metadata normalization methods

   https://doi.org/10.1145/3276774.3276794  

   Koh, Jason; Hong, Dezhi; Gupta, Rajesh; Whitehouse, Kamin; Wang, Hongning; Agarwal, Yuvraj (November 2018, Proceedings of the 5th Conference on Systems for Built Environments)

   The recent advances in the automation of metadata normalization and the invention of a unified schema --- Brick --- alleviate the metadata normalization challenge for deploying portable applications across buildings. Yet, the lack of compatibility between existing metadata normalization methods precludes the possibility of comparing and combining them. While generic machine learning (ML) frameworks, such as MLJAR and OpenML, provide versatile interfaces for standard ML problems, they cannot easily accommodate the metadata normalization tasks for buildings due to the heterogeneity in the inference scope, type of data required as input, evaluation metric, and the building-specific human-in-the-loop learning procedure. We propose Plaster, an open and modular framework that incorporates existing advances in building metadata normalization. It provides unified programming interfaces for various types of learning methods for metadata normalization and defines standardized data models for building metadata and timeseries data. Thus, it enables the integration of different methods via a workflow, benchmarking of different methods via unified interfaces, and rapid prototyping of new algorithms. With Plaster, we 1) show three examples of the workflow integration, delivering better performance than individual algorithms, 2) benchmark/analyze five algorithms over five common buildings, and 3) exemplify the process of developing a new algorithm involving time series features. We believe Plaster will facilitate the development of new algorithms and expedite the adoption of standard metadata schema such as Brick, in order to enable seamless smart building applications in the future. 

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3. A Global Building Occupant Behavior Database

   https://doi.org/10.1038/s41597-022-01475-3  

   Dong, Bing; Liu, Yapan; Mu, Wei; Jiang, Zixin; Pandey, Pratik; Hong, Tianzhen; Olesen, Bjarne; Lawrence, Thomas; O’Neil, Zheng; Andrews, Clinton; et al (December 2022, Scientific Data)

   Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting. 

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4. SeNsER: Learning Cross-Building Sensor Metadata Tagger

   https://doi.org/10.18653/v1/2020.findings-emnlp.85  

   Jiao, Yang; Li, Jiacheng; Wu, Jiaman; Hong, Dezhi; Gupta, Rajesh; Shang, Jingbo (January 2020, Findings of the Association for Computational Linguistics: EMNLP 2020)

   null (Ed.)

   Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e.g., measurement type and location) about sensors for running smart building applications. Unfortunately, sensor metadata in different buildings often follows dis- tinct naming conventions. Therefore, learning a tagger currently requires extensive annotations on a per building basis. In this work, we propose a novel framework, SeNsER, which learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. It leverages the commonality between different buildings: At the character level, it employs bidirectional neural language models to capture the shared underlying patterns between two buildings and thus regularizes the feature learning process; At the word level, it leverages as features the k-mers existing in the fully annotated building. During inference, we further incorporate the information obtained from sources such as Wikipedia as prior knowledge. As a result, SeNsER shows promising results in extensive experiments on multiple real-world buildings. 

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5. Smart Building and Construction Materials

   https://doi.org/https://doi.org/10.1155/2019/2432915  

   Donglu Shi, Julian Wang (January 2019, Advances in materials science and engineering)

   Advances and innovations in materials science and engineering have always played a substantial role in civil engineering, building structural design, and construction. In recent years, extensive effort has been devoted to the applications of stimuli-responsive smart materials and nanostructures in buildings. These smart materials used in the built environment can be defined as those offering specific functional and adaptable properties in response to thermal, optical, structural, and environmental stimuli. Not only do these materials enhance the overall performance of new building construction but also promise safer structures, longer durability of building elements, efficient building energy savings, greater environmental sustainability, and even higher indoor user comfort. Given the increasing imperatives for the above, we have organized this themed special issue that focuses on smart buildings and construction materials. The main aim of this special issue is to encapsulate the current interest and state of research related to the smart materials in building and construction applications, underpinning current and future challenges in building energy, environmental sustainability, and structural safety and durability. 

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