OpenBuildingControl: Digitizing the control delivery from building energy modeling to specification, implementation and formal verification

Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: (1) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and (2) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed. 

A Semantic Ontology for Representing and Quantifying Energy Flexibility of Buildings

Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

/pdf/transformational-iot-sensing-for-air-pollution-and-thermal-3p1m13qq.pdf

Transformational IoT sensing for air pollution and thermal exposures

Current efforts establishing semantic metadata standards for the built environment span academia, industry and standards bodies. For these standards to be effective, they must be clearly defined and easily extensible, encourage consistency in their usage, and integrate cleanly with existing industrial standards, such as BACnet. There is a natural tension between informal tag-based systems that rely upon idiom and convention for meaning, and formal ontologies amenable to automated tooling. We present a qualitative analysis of Project Haystack, a popular tagging system for building metadata, and identify a family of inherent interpretability and consistency issues in the tagging model that stem from its lack of a formal definition. To address these issues, we present the design and implementation of the Brick+ ontology, a drop-in replacement for Brick with clear formal semantics that enables the inference of a valid Brick model from an informal Haystack model, and demonstrate this inference across five Haystack models.

/pdf/formalizing-tag-based-metadata-with-the-brick-ontology-3vezhlmr0w.pdf

Formalizing Tag-Based Metadata With the Brick Ontology

Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders in a smart space are required to deal with communicating with specific IoT devices, capturing data, processing it, and abstracting it out to generate useful inferences. Additionally, this makes reusability of smart space applications difficult, since they are developed for specific sensor deployments. In this article, we present a holistic approach to IoT smart spaces, the SemIoTic ecosystem, to facilitate application development, space management, and service provision to its inhabitants. The ecosystem is based on a centralized repository, where developers can advertise their space-agnostic applications, and a SemIoTic system deployed in each smart space that interacts with those applications to provide them with the required information. SemIoTic applications are developed using a metamodel that defines high-level concepts abstracted from the smart space about the space itself and the people within it. Application requirements can be expressed then in terms of user-friendly high-level concepts, which are automatically translated by SemIoTic into sensor/actuator commands adapted to the underlying device deployment in each space. We present a reference implementation of the ecosystem that has been deployed at the University of California, Irvine and is abstracting data from hundreds of sensors in the space and providing applications to campus members.

https://inria.hal.science/hal-03636539/document

The SemIoTic Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats.To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

/pdf/mortar-an-open-testbed-for-portable-building-analytics-3tpwyhpupm.pdf

Mortar: An Open Testbed for Portable Building Analytics

To combat climate change, we need to change user transportation behavior to be less carbon intensive. Prior work on motivating this behavior change has been predominantly qualitative and lacks comparison. This makes it challenging to determine which interventions should be deployed at scale. The behavior change community needs a process to compare interventions against each other in pilot studies before committing deployment resources. We perform the first quantitative comparison, to our knowledge, of behavior change strategies in the transportation behavior domain. Since this is a pilot with a limited recruitment budget, we design a Randomized Controlled Trial (RCT) using an open source platform. We assign 41 users to three mobile applications: Emotion, Information, Control. The RCT allows us to draw statistically valid inferences that can suggest future avenues for larger-scale studies. We found that Emotion resulted in greater engagement with the application (p=0.006, 0.035, 0.031, 0.040) while Information improved the sustainability of travel behavior (p = 0.043). These exploratory statistical results can motivate the design of future studies to further explore combinations of these approaches for sustainable transportation behavior.

https://dl.acm.org/doi/pdf/10.1145/3360322.3360871

TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications

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Mortar: An Open Testbed for Portable Building Analytics

TripAware: Emotional and Informational Approaches to Encourage Sustainable Transportation via Mobile Applications