Energy Technologies

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.

This paper tests a methodology proposed by a U.S. government inter-agency working group for calculating a facility-level U.S. national average greenhouse gas emissions intensity for a selected traded good. The testing of the method relies on publicly available data from the U.S. government supplemented by for purchase data. To draw practical insights, a pilot product is selected for this initial test (cold-rolled stainless steel with width less than 600mm). Lessons learned based on the results of testing the method with this product include: 1) the level of product specificity chosen should consider the emissions and production data availability for the product, particularly with respect to the product’s supply chain, production pathways, and any co-products from its production, and 2) more granular, nonpublic micro-data collected by the U.S. government include additional relevant details that may address gaps in publicly available data and hold promise towards successfully applying the U.S. government proposed method. This paper is intended to provide a preliminary assessment of data availability, potential pathways for calculating scope 1 and scope 2 emissions from industrial facilities, and related challenges and potential remedies. As such, it draws its findings based on testing one product. Extension of any findings from this paper should be corroborated with results from testing additional products. Findings reported in this paper are preliminary and meant to inform potential future work to assess data needs and availability for determining facility-level greenhouse gas emissions intensity of a traded good. This paper is the first of an envisioned series reporting on methods for bench-marking facility-level greenhouse gas emissions intensities of U.S. products.

Architectural thermo-responsive dynamic windows offer an autonomous solution for solar heat regulation, thereby reducing building energy consumption. Previous work has emphasized the significance of thermo-responsive windows in hot climates due to their role in solar heat control and subsequent energy conservation; conversely, our study provides a different perspective. Through a global-scale analysis, we explore over 100 material samples and execute more than 2.8 million simulations across over two thousand global locations. World heatmap results, derived from well-trained artificial neural network models, reveal that thermo-responsive windows are especially useful in climates where buildings demand both heating and cooling energy, whereas thermo-responsive windows with optimal transition temperatures show no dynamic features in most of low-latitude tropical regions. Additionally, this study provides a practical guideline and an open-source mapping tool to optimize the intrinsic properties of thermo-responsive materials and evaluate their energy performance for sustainable buildings at various geographical scales.

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.

Navigating the broad and rapidly evolving market landscape of software solutions is complex whether you are a sustainability leader, building owner, energy manager, or building engineer with energy and greenhouse gas (GHG) emissions reduction goals for a portfolio of buildings. The Department of Energy’s Better Buildings partners have noted this complexity and the associated lack of publicly available information. In response, this report reviews the ecosystem of environmental, social, and governance (ESG), energy management information systems (EMIS), and decarbonization software with the goal of orienting prospective users to current offerings. Organizations can utilize this guidance to determine the specific capabilities needed to support decarbonization efforts and procure appropriate software to streamline the GHG emissions reduction process. In this paper, we refer to “decarbonization software” as the category of software that meets an organization’s needs for decarbonization planning, implementation, and tracking. This software may have a heritage in ESG or EMIS, or it may be an entirely new product. This report offers a snapshot of today’s rapidly evolving decarbonization software capabilities, along with guidance for procuring and utilizing it that will remain relevant despite any future software changes. Exploratory research was conducted on over 100 software providers, and interviews were held with 28 of them. Note that inclusion in this report does not indicate an endorsement, nor does a product’s absence from this report indicate a lack of suitability

Decarbonizing the US residential building stock requires a substantial acceleration in home energy upgrades. Numerous barriers exist to accelerating adoption of efficient and electric building technologies, but foremost among these is high upfront costs. This study uses an industry survey delivered to a sample of home energy professionals to examine promising cost reduction strategies across a range of project types, including HVAC, water heating, and envelope/insulation projects. The survey included quantitative and qualitative questions to collect evidence on the estimated cost reduction potential of these strategies and their likelihood of use in the construction industry. The 167 survey respondents included contractors, energy consultants, architects, manufacturers, and others with experience in delivering energy upgrades in single-family and multifamily buildings in the US. Results show that significant cost reductions are achievable by minimizing additional infrastructure costs (such as replacing electric panels), streamlining project planning/management, and deploying innovations that simplify installation. We find that for a typical deep retrofit project, including heat pumps for space and water heating in addition to envelope upgrades, the strategies could result in a total installed cost reduction of nearly 50 %, dramatically improving the customer economics of such a project. This research makes a novel contribution to the literature on strategies to reduce the costs of residential retrofits. We discuss how our study's insights on the highest-value cost reduction strategies for home energy upgrades can further accelerate their uptake in the US housing stock.

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.