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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.

Zhao, Junbo↗
DOE OSTI2020

Joint Optimization of Well Completions and Controls for CO 2 Enhanced Oil Recovery and Storage

CO 2 storage through CO 2 enhanced oil recovery (EOR) is considered as one of the technologies to help promote larger scale deployment of CO 2 storage because of associated economic benefits through oil recovery, 45Q tax credits and the utilization of existing infrastructure. The objective of this study is to demonstrate how optimal reservoir management and operation strategies (including well completions and controls) can be used to optimize both CO 2 storage and oil recovery. The optimization problem was focused on jointly estimating the well completions (i.e., fraction of injection/production well perforations in each reservoir layer) and CO 2 injection/oil production controls that maximize the net present value (NPV) in a CO 2 EOR and storage operation. We utilized the newly developed StoSAG algorithm, one of the most efficient optimization algorithms in the reservoir management community, to solve the optimization problem. The performance of joint optimization approach was compared with the performance of well control only optimization approach. In addition, the performance of co-optimization of CO 2 storage and oil recovery approach was compared with the performances of maximization of only CO 2 storage and maximization of only oil recovery approaches. The optimization results showed that a joint optimization of well completions and well controls can achieve an 8.84% higher final NPV than the one obtained from the optimization of only well controls. It was observed that the NPV incremental for joint optimization is mainly due to the fact that the optimal well completions and controls approach results in efficient CO 2 storage and oil production from different reservoir layers depending on the differences in individual layer properties. Comparison of co-optimization (i.e., maximization of NPV) and maximization of only CO 2 storage or only oil recovery showed that the co-optimization and maximization of only oil recovery result in significantly higher final NPV than that obtained through maximization of only CO 2 storage approach while maximization of only CO 2 storage can achieve significantly higher CO 2 storage in the reservoir compared to the other two scenarios. The similar results for co-optimization and maximization of oil production are obtained because of the difference in oil revenue compared to CO 2 storage tax credit. To the best of our knowledge, this is the first study in oil/gas industry and CO 2 storage community to perform joint optimization of well completions and well controls in the fields. We expect that the proposed optimization framework will be a useful and efficient tool for field engineers to optimally manage CO 2 EOR projects to maximize revenue through oil recovery as well as CO 2 storage by taking advantage of the new 45Q tax law.

Artificial intelligence↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗
DOE OSTI2021

Evaluation of Centralized Model Based FLISR in a Lab Setup

Utilities are installing advanced distribution management systems (ADMS) around the globe to improve the sensing and control of distribution systems. ADMS is becoming a critical component to improve the resiliency and reliability of distribution systems. These management systems host a multitude of applications that can be used to sense, control, and operate distribution systems. Fault location, isolation, and service restoration (FLISR) is one of the ADMS applications that is critical to improving resilience during fault conditions. FLISR applications use smart, controllable devices that are installed in the distribution system for FLISR operation. These controllable devices include distributed energy resources (DER) and reclosers. Evaluating such ADMS applications before installation in the field can help de-risk the field implementation and avoid costly failures in the field. This paper presents a background on experimental setup that are typically used to evaluate ADMS applications. This is followed by briefly presenting the setup used to evaluate the FLISR application in an off-the-shelf ADMS tool. Finally, results from the evaluation experiments are presented.

94 GMLC - Grid Modernization Laboratory Consortium↗
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Use of Historical EM Practices and Policies for Management of Cleanup Program and Address GAO Concerns - 20222

As Department of Energy (DOE) Environmental Management (EM) program policies and management practices are revised over time, it might be useful to periodically reflect on the DOE EM program's past to identify practices that have worked and those that might be appropriate for possible reuse in the future. An added impetus to examine alternative approaches is the recent Government Accountability Office (GAO) report entitled 'Nuclear Waste Cleanup - DOE Could Improve Program and Project Management by Better Classifying Work and Following Leading Practices.' The February 2019 GAO report states that EM categorizes most of its work in a way that does not adequately involve independent experts and DOE senior leadership. It also noted that EM has not followed leading practices for program and project management that could help keep the cleanup efforts on schedule and control costs. The GAO report recommended that EM, working with DOE Office of Project Management (PM): - Establish requirements and then assess EM's ongoing operations activities to determine if some activities should be reclassified as capital asset projects based on these newly established requirements. - Review and revise EM's 2017 cleanup policy to include program management leading practices related to scope, cost, schedule performance, and independent reviews. - Update the cleanup policy to require that earned value management (EVM) systems be maintained and used in a way that follows EVM best practices. - Develop a policy to ensure that work is categorized as level of effort (LOE) only in appropriate, specified circumstances, such as when work is not measurable or when measurement is impractical. - Integrate EVM data into EM's performance metrics for operations activities. To assist with improving program performance and implementing audit recommendations, this paper reviewed past successful cleanups such as Weldon Springs, Rocky Flats, Fernald, Ashtabula, Battelle Columbus (abs, and other historical resources and people with experience during the early years of EM program. As such, there are several past practices which may well be suited for reuse today. Some of these practices include: - In 1990's, as the DOE EM cleanup program, known as Environmental Restoration came into effect, the known cleanup scope at each geographic location or site was organized into four levels ranging from overall site-wide cleanup effort to the individual 'release sites', providing high granularity to the program. - The cleanup program was organized by geography (Northwest, Southwest, Eastern Area Programs, etc.). These 'sub-program' managers provided a degree of Headquarters oversight and management allowing upper management (EM-1, 2, and 3) to focus on major decisions and issues. - Budget levels at selected cleanup sites were funded above 'minimum-safety' levels to accelerate actual cleanup work. - Past practice utilized EVM techniques and uniform EM oversight over all discrete project-like activities, providing for a coordinated cleanup effort. - EM managed LOE activities using benchmarking and best in class management practice techniques to help manage costs. - EM managed and reviewed entire site cleanup baselines, not only individual projects, focusing on a successful overall outcome. - Sites had a 'Road map' or 'Management Action Plan' that provided the overall strategy or plan for that site's cleanup - this helped with understanding and coordination of efforts. - EM had a robust lessons-learned program and database to capture and transmit worthy ideas. - EM had well-structured cleanup baseline change control policies to control cost and schedule changes, at the contractor and higher Headquarters levels. Finally, some of the GAO observations may be valid, however it is not clear as to their extent. For example, for some operation types of work, the use of the Level of Effort (LOE) earned value method to track progress may be appropriate. Also, to ensure independence of thought, and to obtain useful insight on past practices, it may be suitable to have former EM employees to assist with understanding past practices or to verify that activities have been properly identified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The Decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Model Quality and Measurement Density Impact on Volt/Volt Ampere Reactive Optimization Performance

The operation of the utility grid is being reshaped by the continuous addition of distributed energy resources and advanced metering infrastructure, which challenge existing grid control strategies. Some utilities deploy advanced distribution management systems (ADMS) to assist with the consolidation of various applications and to augment situational awareness in response to the new power delivery dynamics. An ADMS is an integrated software platform that provides utilities with a way to enhance their reliability, control, and optimization with advanced applications, such as volt/VAR optimization (VVO). A VVO application could serve as a vehicle to deliver cost savings by providing the utility with a method to reduce rates by controlling the voltage and decreasing the energy usage in their service territory. Some utilities are reluctant to integrate an ADMS, because it is a significant investment that requires approval from the public regulatory commission and/or their customers. This paper evaluates the impact on VVO performance when using a lower-quality network model supplemented with additional measurements, which could provide an implementation for cost savings. The results show that a better model quality would provide the highest energy savings; however, some level of telemetry is necessary in all scenarios to prevent voltage exceedances.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Cost-optimized energy storage operation for a grid-connected solar PV system at community and individual scales

This study provides a comparative analysis of grid-connected PV-integrated battery storage at individual and community scales. The paper addresses the challenge of managing energy demand-generation mismatch by using a battery energy storage optimization algorithm, which minimizes operational costs while accounting for battery degradation. Also, this work introduces a broader evaluation basis that includes seasonal variability, grid exchange smoothness, and scalability across different battery capacities. Results show that community-scale storage more effectively dampens grid exchange power fluctuations and reduces system costs, particularly with moderate price differences between electricity buying and selling prices and low battery capacities. The paper also analyzes the impacts of static control versus cost-optimized battery system management. Here, it is shown that the gap in system costs between the cost-optimized and static control scenarios widens as the price difference increases.

25 ENERGY STORAGE↗
DOE OSTI2022

Electric Access System Enhancement (EASE): Assessment of a Distributed Energy Resource Management System for Enabling Dynamic Hosting Capacity

The Electric Access System Enhancement (EASE) project demonstrated a scalable, interoperable, and cost-effective means of integrating high penetration of distributed energy resources (DERs). The control architecture developed leveraged a Distributed Energy Resource Management System, Distribution System Operator for transacting energy, and 3rd Party DER Aggregator platform. The project identified ways to enhance the customer interconnection process to the grid and improve access to information from DERs and optimize the usage of DERs to provide energy services in a simulated day-ahead shadow market while maintain grid reliability. By integrating these capabilities into a scalable system of systems, the DSO can effectively balance DER generation and customer demand on the distribution network. This capability allows the grid to host more DER than traditionally possible on wires alone. Hosting more DER has the added benefit of supplying increased demand growth, sometimes beyond the capacity limits of the distribution network itself. This is known as a dynamic hosting capacity, which could help utilities manage the forecasted growth in electricity demand as California switch to electric vehicles and appliances. This could help to establish energy storage and PV generation as a “pseudo firm” generation resource mix if managed appropriately and provide sufficient resource adequacy for distribution capacity upgrade deferrals.

14 SOLAR ENERGY↗
DOE OSTI2025

Architectural Approaches for Integrating ADMS and DERMS: Challenges, Comparisons, and Real-World Use Cases

The electrical distribution landscape is rapidly transforming due to the proliferation of distributed energy resources (DERs) such as solar panels, wind turbines, battery storage systems, combined heat and power units, and electric vehicles, introducing variability and uncontrollability that traditional grid operators are ill-equipped to manage. This transformation is further accelerated by advancements in Information and Communication Technology infrastructure that connects control centers with end devices, demanding automation and a deeper understanding of new technologies by utility personnel. Advanced grid control techniques using system-level optimization, Artificial Intelligence, and Machine Learning at the enterprise level and distributed level are evolving to address these issues. There is also an opportunity to utilize the enormous data created by these new DER technologies in the grid. Advanced Distribution Management Systems (ADMS) and Distributed Energy Resource Management Systems (DERMS) are critical in addressing these challenges by automating grid operations and enhancing reliability. Given the relatively recent development of ADMS and DERMS, and the still relatively low level of ADMS and DERMS deployment in the industry, there is a notable deficiency in the comprehensive understanding of the challenges and benefits associated with these new technologies, especially with their complementary natures and integration architectures. This paper aims to bridge the knowledge gap in ADMS and DERMS integration, presenting three distinct integration architectures currently available, and discussing the challenges and benefits of each architecture to guide utilities, industry professionals, and researchers in optimizing grid management and decision-making processes for a resilient and efficient energy future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Integrating a Microgrid Controller with a Local OpenADR Server

When military microgrids isolate themselves from the main electrical grid, they must locally balance electricity supply and demand. Since local generation may be limited, the current strategy is to shed all but the most critical loads by tripping smart circuit breakers, which must then be reset manually (e.g., ESTCP project EW-201350). This strategy is typically applied at the building level, meaning that entire buildings housing mission critical activities must be excluded from any load management, while those considered non-critical may lose service entirely. The remotely controlled switchgear needed to manage load in this way is very expensive ($\$30,000$-$\$50,000$ per building). While effective at shedding load, this strategy disrupts installation operation and risks damaging equipment during both disconnection and re-energization. With the goals of lowering costs, protecting equipment, and enhancing the agility of DoD microgrids, this report demonstrates the use of cybersecure automated demand response (ADR) technology to manage microgrid loads during grid-independent (a.k.a. "islanded") operation. This automated approach achieves load shedding and shifting through communication signals sent to equipment controllers rather than by cutting off the flow of electricity within the microgrid itself. Because it operates only on the base network, with no connection to external entities, this strategy avoids the main cybersecurity concern raised by past applications of ADR on military bases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2025

Review of Reactor Facilities without Main Control Rooms

Small, advanced reactors may require few, if any, safety-related human actions (HAs) and fewer HAs to monitor and control the plant. In comparison to large light water reactors, these are significant changes that have implications for many aspects of an applicant's human factors engineering (HFE), including control room design, plant staffing, and the management of safety functions. To support the Nuclear Regulatory Commission's (NRC) ability to evaluate these changes, information needs to be developed addressing the characteristics and potential issues. The objectives of our research were to (1) identify when a traditional main control room (MCR) may not be necessary, (2) identify workplace design alternatives to traditional MCRs, and (3) develop guidance for reviewing an applicant's workplace designs with and without a MCR. We determined that the safety question isn't so much justifying why a design has no MCR, but rather verifying that important human actions can be accurately and reliably performed under a range of challenging conditions using the HSIs provided regardless of their location. We developed guidance to review alternatives to MCRs based on HFE analyses for determining workplace location and design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Breakthrough innovations in carbon dioxide mineralization for a sustainable future

Greenhouse gas emissions and climate change concerns have prompted worldwide initiatives to lower carbon dioxide (CO 2 ) levels and prevent them from rising in the atmosphere, thereby controlling global warming. Effective CO 2 management through carbon capture and storage is essential for safe and permanent storage, as well as synchronically meeting carbon reduction targets. Lowering CO 2 emissions through carbon utilization can develop a wide range of new businesses for energy security, material production, and sustainability. CO 2 mineralization is one of the most promising strategies for producing thermodynamically stable solid calcium or magnesium carbonates for long-term sequestration using simple chemical reactions. Current advancements in CO 2 mineralization technologies, focusing on pathways and mechanisms using different industrial solid wastes, including natural minerals as feedstocks, are briefly discussed. However, the operating costs, energy consumption, reaction rates, and material management are major barriers to the application of these technologies in CO 2 mineralization. Further, the optimization of operating parameters, tailor-made equipment, and smooth supply of waste feedstocks require more attention to make the carbon mineralization process economically and commercially viable. Here, carbonation mechanisms, technological options to expedite mineral carbonation, environmental impacts, and prospects of CO 2 mineralization technologies are critically evaluated to suggest a pathway for mitigating climate change in the future. The integration of industrial wastes and brine with the CO 2 mineralization process can unlock its potential for the development of novel chemical pathways for the synthesis of calcium or magnesium carbonates, valuable metal recovery, and contribution to sustainability goals while reducing the impact of global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗