1,720,988 research outputs found
A holistic time series-based energy benchmarking framework for applications in large stocks of buildings
Full text linkWith the proliferation of Internet of Things (IoT) sensors and metering infrastructures in buildings, external energy benchmarking, driven by time series analytics, has assumed a pivotal role in supporting different stakeholders (e.g., policymakers, grid operators, and energy managers) who seek rapid and automated insights into building energy performance over time. This study presents a holistic and generalizable methodology to conduct external benchmarking analysis on electrical energy consumption time series of public and commercial buildings. Differently from conventional approaches that merely identify peer buildings based on their Primary Space Usage (PSU) category, this methodology takes into account distinctive features of building electrical energy consumption time series including thermal sensitivity, shape, magnitude, and introduces KPIs encompassing aspects related to the electrical load volatility, the rate of anomalous patterns, and the building operational schedule. Each KPI value is then associated with a performance score to rank the energy performance of a building according to its peers. The proposed methodology is tested using the open dataset Building Data Genome Project 2 (BDGP2) and in particular 622 buildings belonging to Office and Education category. The results highlight that, considering the performance scores built upon the set of proposed KPIs, this innovative approach significantly enhances the accuracy of the benchmarking process when it is compared with a conventional approach only based on the comparison with the buildings belonging to the same PSU. As a matter of fact, an average variation of about 14% for the calculated performance scores is observed for a testing set of building
A performance-based incentive sharing mechanism for communities of residential end users leveraging an ontology-driven approach
Full text linkEnergy sharing, whether physical or virtual, is crucial for optimizing the use of locally generated renewable energy within communities of residential end users, including Renewable Energy Communities (RECs) and Collective Self-Consumption (CSC) groups. By sharing energy, participants can increase self-consumption of renewables while reducing reliance on the grid. To encourage participation, many frameworks provide economic incentives for shared energy, offering financial benefits to those who contribute to community energy goals. However, ensuring a fair allocation of both shared energy and its associated incentives remains a challenge. This study introduces a novel performance-based incentive-sharing mechanism that dynamically adjusts the allocation of economic benefits based on user ability to shift consumption in response to surplus availability. Different from traditional approaches, the mechanism integrates a dynamic baseline selection process with an ontology-driven metadata model, using SAREF and its domain-specific extensions to ensure interoperability and automation. This semantic framework enables scalable deployment across heterogeneous community configurations while reducing setup complexity. The process was tested over a seven-month period within a collective self-consumption group of 13 residential users who virtually share energy from a centralized PV system. Results show that users who adjusted their consumption to match surplus availability increased their daily incentives by up to 40% compared to a standard sharing mechanism, while those who performed below expectations experienced a corresponding decrease. These findings highlight the potential of structured data-driven approaches, supported by ontologies, to improve decision-making in community energy management
A Data-Driven Process for Optimal Incentive Sharing in Collective Self-Consumption Groups of Residential Users
No full textWith the widespread adoption of renewable energy systems in residential buildings, particularly in the context of collective self-consumption groups (CSC) and Renewable Energy Communities (REC), understanding user behavior becomes pivotal for enhancing energy efficiency and increasing the energy share among participants for an optimal use of renewable resources. Regardless of which configuration is adopted (CSC or REC), a key aspect is how to share the generated economic benefits from the self-produced energy and identify the fairest way to distribute the incentive derived from the shared energy among users. In this context, the aim of this work is to introduce a data-driven energy benchmarking process that leverages the analysis of long-term monitoring data of residential buildings to i) characterize energy consumption patterns of users over time, ii) support the development of an optimal incentive sharing mechanism among users involved in such legal entities. The proposed approach is tested on a monitored residential building, located in Northern Italy, which includes 13 flats and is equipped with a centralized photovoltaic system
sj-tiff-1-jet-10.1177_15266028231179864 – Supplemental material for Total Transfemoral Branched Endovascular Thoracoabdominal Aortic Repair (TORCH2): Short-term and 1-Year Outcomes From a National Multicenter Registry
No full textSupplemental material, sj-tiff-1-jet-10.1177_15266028231179864 for Total Transfemoral Branched Endovascular Thoracoabdominal Aortic Repair (TORCH2): Short-term and 1-Year Outcomes From a National Multicenter Registry by D’Oria Mario, Grandi Alessandro, Pratesi Giovanni, Parlani Gianbattista, Giudice Rocco, Gargiulo Mauro, Mangialardi Nicola, Chiesa Roberto, Lepidi Sandro and Bertoglio Luca in Journal of Endovascular Therapy</p
sj-pptx-2-jet-10.1177_15266028231179864 – Supplemental material for Total Transfemoral Branched Endovascular Thoracoabdominal Aortic Repair (TORCH2): Short-term and 1-Year Outcomes From a National Multicenter Registry
No full textSupplemental material, sj-pptx-2-jet-10.1177_15266028231179864 for Total Transfemoral Branched Endovascular Thoracoabdominal Aortic Repair (TORCH2): Short-term and 1-Year Outcomes From a National Multicenter Registry by D’Oria Mario, Grandi Alessandro, Pratesi Giovanni, Parlani Gianbattista, Giudice Rocco, Gargiulo Mauro, Mangialardi Nicola, Chiesa Roberto, Lepidi Sandro and Bertoglio Luca in Journal of Endovascular Therapy</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs
Full text linkOne of the key challenges of Energy Management and Information Systems in buildings is related to the lack of interoperability, due to the absence of standardization of the underlying data models. In recent years, there has been a growing interest in using ontology-based metadata models to address this issue, as they offer a structured approach to organize and share information across diverse systems (e.g. Brick ontology). However, the creation of ontology-based metadata models is often a labor-intensive task that requires specific domain expertise, hindering the practical use of such data models. For this reason, in this work the BrickLLM Python library is introduced, which addresses this issue by generating Brick-compliant Resource Description Framework graphs through Large Language Models, automating the process of converting natural language building descriptions into machine-readable metadata. The library supports both cloud-based APIs (e.g., OpenAI, Anthropic, Fireworks AI), local models (e.g. LLaMa3.2, etc.) and evenfine-tuned ones. This paper explores the architecture, key functionalities, and practical applications of BrickLLM, showcasing its potential impact on the future of building systems monitoring and automation
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A national cross-sectional survey on time-trends for endovascular repair of genetically-triggered aortic disease and connective tissue disorders over two decades
No full textBackground: By this survey, we aim to gain national-based information regarding trends in endovascular repair (ER) for the treatment of aortic disease in patients with genetically-triggered aortic disease (GTAD) and connective tissue disorder (CTD) over the last two decades. Methods: All Italian vascular surgery centers (N.=80) were invited to participate in an anonymous electronic cross-sectional survey on ER for GTAD/CTD. Results: Overall, 29 institutions completed the survey, thereby yielding a 36% response rate. The percentage of responding institutions rises to 64% if only regional hubs were considered (23/36). The median number of index procedures per center was 6.2, and a steady increase in the overall number of interventions over time was also noted. Most patients were males (73%) with a median age of 48 years. The most common endovascular procedure was TEVAR (N.=101), followed by F/BEVAR (N.=43) and EVAR (N.=37). The overall technical success rate was 83.4% while major adverse events and mortality at thirty days were reported at 18.2% and 9.9%, respectively. An additional 5.0% mortality rate was noted for an overall one-year mortality of 14.9%, while 3.7% of all treated patients were diagnosed with a type 1 endoleak. Conclusions: This national cross-sectional survey, investigating trends in ER of GTADs and CTDs over two decades, highlights a consistent increase in the use of endovascular techniques for their treatment. Early mortality was acceptably low, yet influenced by the urgency of presentation. At one-year follow-up, a 5% additional death rate was noted, and the reintervention rate remained below one in ten
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