Energy Technologies

Faults in heating, ventilation and air conditioning systems can lead to increased energy consumption, occupant comfort issues, and reduced equipment lifetime. Commercial fault detection and diagnosis (FDD) tools has been increasingly deployed in U.S. commercial buildings. While they are helping to achieve energy efficiency and operational reliability, there remain gaps in their fault diagnostic capabilities. The diagnostic results often contain multiple distinct candidate root causes (CRCs) or offer no insight into CRCs. This study developed a novel active rule-based multi-mode data analysis method to enhance diagnostic resolution by applying proven rule sets and additional new rules to data from multiple known operational modes. The proposed method was demonstrated using enhanced air handling unit performance assessment rule sets and validated with the simulated data of two air handling units. New metrics, namely, reduced number of CRCs and improvement ratio, were developed to quantify the improvement of fault diagnostic resolution. The validation results showed that the proposed method effectively reduced the number of CRCs in contrast to analyzing data solely for a single mode of operation. It achieved a median improvement ratio of 80% in 19 test cases.

Grid-interactive efficient buildings (GEBs) with flexible loads are a promising method to decarbonize buildings and support the grid. Despite the promising benefits of GEBs, automation systems managing flexible loads in response to grid signals are still uncommon in US commercial buildings. Recent literature showcases control solutions of this nature; however, they frequently depend on customized integrations that lack the essential tools and drivers required for scalability. To address these gaps, we created a workflow and software platform, called Open Building Operating System (OpenBOS). The software uses ASHRAE standard 223 P to improve scalability of GEB applications. It also provides access to a virtual testing environment, enabling the evaluation of control algorithms prior to their deployment in the field to mitigate the risk of system malfunctions or underperformance. Using the workflow and OpenBOS platform, two GEB applications were developed, deployed and tested in a simulated environment and a real building. Notably, the GEB applications significantly reduced energy costs by 28% and 23% respectively, compared to the baseline. Furthermore, amidst a shed event, demand savings amounted to 45% and 47%, while ensuring a minimal impact on comfort. The paper outlines insights gained and potential avenues for future research stemming from these novel tools.

Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution and then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional-integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

Weather plays a significant role in building operations as it directly influences HVAC loads and in turn the building energy and thermal performance. In a changing climate, future trends and extreme weather events become critical concerns in the global building decarbonization and clean energy transition. This paper aims to address ten key questions concerning extreme and future weather data for building applications, and more importantly to identify research gaps and guide the curation and selection of future and extreme weather data for use in building performance simulation and assessment. The paper intends to inform architects and engineers, operators, owners, policy makers, and other stakeholders on considering the impacts of future and extreme weather data and adopting strategies for selecting and applying this data in various use cases related to building design, operation, and retrofit for energy efficiency, electrification, and climate resilience.

Buildings account for more than one third of global energy consumption and carbon emissions, making the optimization of their energy use crucial for sustainability. Advanced Metering Infrastructures data offers a rich source of information for understanding and improving building energy performance, yet existing frameworks for leveraging this data are limited. This paper presents a comprehensive data-mining framework for analyzing Advanced Metering Infrastructures data at multiple temporal and spatial scales, beneficial for building owners, operators, and utility companies. Utilizing hourly electricity consumption data for the east region of Portland, Oregon, the study systematically extracts key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. The framework employs a list of techniques including load-level detection, home vacancy detection, and weather-sensitivity analysis and statistical methods to provide detailed insights into building energy dynamics. As an unsupervised study, it reveals patterns and trends without predefined labels or categories. Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. The insights gained can potentially inform better energy management strategies, support grid operations and planning, guide policy-making for energy efficiency improvements, as well as improve input and assumptions in the building energy modeling. This study opens pathways for future research, including integrating more data sources and collaborating with utility companies to validate hypotheses and further explore building energy use insights.

In recent years, Transfer Learning (TL) has emerged as a promising solution to scale Deep Reinforcement Learning (DRL) controllers for building energy management, addressing challenges related to DRL implementation as high data requirements and reliance on surrogate models. Moreover, most TL applications are limited to simulations, not revealing their real performance in actual buildings. This paper explores the implementation of an online TL methodology combining imitation learning and fine-tuning to transfer a DRL controller between two real office environments. Pre-trained in simulation using a calibrated digital twin, the DRL controller reduces energy consumption and improves indoor temperature control when managing the operation of a Thermally Activated Building System in one of the two offices both in simulation and in the real field. Afterwards, the DRL controller is transferred to the other office following the online TL methodology. The proposed approach outperforms a DRL controller implemented without pre-training, and Rule-Based and Proportional-Integral controllers, achieving energy savings between 6 and 40% and improving indoor temperature control between 30 and 50%. These findings underscore the efficacy of the online TL methodology as a viable solution to enhance the scalability of DRL controllers in real buildings.

This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.