Engineering topics

Roa, Carlos Duarte

Publications and source records attributed to Roa, Carlos Duarte.

5 records

DOE OSTI2023

Field Demonstration of the Brick Ontology to Scale up the Deployment of ASHRAE Guideline 36 Control Sequences

Many commercial buildings have a vast network of sensors as part of their building automation systems (BAS) that allows opportunities for energy consumption and cost savings by deploying advanced control sequences. However, this resource is often underutilized since BAS are typically programmed with simple control sequences with limited potential to deliver on these opportunities. The recent availability of ASHRAE Guideline 36 (G36) with advanced HVAC control sequences supports control retrofits in existing buildings to unlock much of the savings potential. However, barriers such as the lack of standard naming convention of building assets and data points, proprietary equipment and BAS, and the inherent uniqueness of buildings and their systems prevent building stakeholders from adopting any “plug-and-play” implementation of G36. Instead, control vendors must often undertake the manual and labor-intensive point mapping process to identify a data stream’s functional and spatial relationship within the HVAC system along with other relevant contexts and map it to the new control sequences. The vendor must carry out the point mapping process in each individual building since the mapping is unlikely to port over to another building. Even for the same building, the point mapping process can occur multiple times if various control vendors implement different control retrofits and/or multiple control retrofits happen over the lifecycle of the building. Then, there is the likelihood that G36 control sequences are programmed uniquely to the building, preventing the same implementation from being reused in another. Therefore, this paper presents a field demonstration of how we leveraged the Brick ontology with BACnet, OpenBuildingControl’s Control Description Language (CDL), and open-source support tools to implement scalable and portable advanced building controls. These tools provide standardized semantic descriptions and relationships of the building’s assets and data points (Brick), standardized communication protocol to read from and write to the building’s BAS (BACnet), and standardized code implementations (CDL) of standardized advanced control strategies (G36). We implemented G36’s hot water supply temperature setpoint reset in a Berkeley, CA building for this field demonstration. This field demonstration aims to show how integrating these tools may streamline the deployment of advanced control sequences such as G36 in a consistent manner regardless of differences found across buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Detecting Passing Valves at Scale Across Different Buildings and Systems: A Brick Enabled and Mortar Tested Application

Heating hot water distribution systems are typically used in commercial buildings to condition spaces to provide occupant thermal comfort. However, recent research shows significant distribution losses within these systems that drive down the overall hot water plant efficiency. This research focuses on detecting passing valves in reheat coils found in variable air volume (VAV) terminal units to reduce distribution losses. A passing valve allows hot water flow when the actuator on the valve is commanded to be closed. The fluid causes unintentional heating or cooling to occur, causing comfort and control issues, and wasting energy. We developed the passing valve detection algorithm using a framework based on the Brick schema and Mortar platform to ensure that the application is portable and can scale to many buildings. We applied the same application to analyze 1,335 VAV reheat terminal units in 20 buildings. The diversity found in these large datasets increases confidence that any building with VAV reheat terminal units with the required sensors and Brick data model can run our open-source algorithm with little or no modification. In aggregate, 5% of VAV units analyzed were categorized as having a sensor fault, 14% with potential passing valve fault, and 81% with no faults detected. However, there is a significant variation in the proportion of VAV units with a passing valve detected (1% to 83%) of each building’s analyzed units.

Roa, Carlos Duarte↗

DOE OSTI2022

Towards a Stronger Foundation: Digitizing Commercial Buildings with Brick to Enable Portable Advanced Applications

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.

Roa, Carlos Duarte↗

DOE OSTI2021

Query relaxation for portable brick-based applications

Semantic metadata standards pave the way for interoperability by providing building operators and application developers with common schemes to describe building resources. Applications can query building metadata models to retrieve the set of entities and relationships they need to operate, instead of hard-coding references to specific points and objects from the underlying data sources. Currently, querying such models requires the developer to be very specific when formulating queries in order to obtain meaningful answers (or any answer). The developer is inevitably expected to be familiar with the systems and components of the buildings being queried, as well as the schema used to represent them. The variety of buildings - both in the composition of their subsystems and in how they happen to be modeled - means that the developer will need to use multiple queries in order to retrieve necessary results. This is complex, time-consuming and error-prone. To address this limitation, we investigate query relaxation as a technique to facilitate discovery of meaningful building resources in a collection of ontology-based buildings data. We evaluate our query relaxation approach over a set of Brick models and demonstrate its use in the context of real-world building applications.

Brick↗