1,720,955 research outputs found
Physics-Informed vs. Deep Learning: Indoor Temperature Prediction with Different Data Availability
Get PDFReducing energy consumption in the building sector is crucial for global sustainability. Achieving energy efficiency requires advanced technologies and robust building models that address uncertainties and environmental variations. White-box models, based on physical principles, provide interpretability but require significant expertise and resources. In contrast, black-box models rely on historical data, lacking interpretability and being dataset-dependent. To bridge this gap, Scientific Machine Learning integrates physical insights into machine learning frameworks, ensuring interpretability while maintaining the accuracy and computational efficiency of models like neural networks. This work incorporates physical knowledge through Ordinary Differential Equations into the neural network framework, developing a physics-informed model to predict indoor air temperature based on external conditions, system thermal powers, and building internal gains. Datasets from four cities with diverse climatic conditions were used, and the model was trained on varying amounts of data, from two weeks to two years. This approach offers a novel exploration of model performance under different data availability and multiple scenarios. A comparative analysis with a Long Short-Term Memory neural network shows that, especially with limited training data, the Physics-Informed Neural Network outperforms the conventional model, with a Mean Absolute Error up to 0.69°C lower. This advantage is due to the incorporation of physics-based constraints, reducing reliance on large datasets. Additionally, the Physics-Informed Neural Network demonstrates stable accuracy across seasonal and uncontrolled dynamics conditions, highlighting its potential for temperature prediction and building control applications
BIM-to-BEM Framework for Energy Retrofit in Industrial Buildings: From Simulation Scenarios to Decision Support Dashboards
Get PDFThe digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments
A BIMtoBEM Framework for Building Retrofit and HVAC Smart Control Assessment
Get PDFAchieving climate neutrality requires a fundamental transformation of production systems and energy use, driven by technological innovation. In the building sector, virtual representations of physical assets can accelerate this transition by enabling simulation-based evaluation of energy strategies. When combined with Reinforcement Learning (RL), these models support dynamic testing and real-time optimization of building operations. This study presents a simulation framework for assessing and comparing energy management strategies aimed at reducing energy consumption while maintaining thermal comfort. As a case study, the methodology is applied to an existing industrial facility using the BIMtoBEM modeling approach. The framework integrates detailed simulation models with RL-based control to optimize the performance of the Heating, Ventilation, and Air Conditioning (HVAC) system. Two digital models with increasing Levels of Detail, are developed to evaluate the impact of three structural and one mechanical refurbishment scenario, alongside two RL control strategies. By simulating different combinations of physical retrofits and control approaches, the framework enables users to identify the most impactful interventions and make informed decisions based on specific energy-saving goals. Results show that modifying the mechanics of the HVAC system alone leads to a 12% reduction in natural gas consumption, while combining retrofitting with RL can lead to 32% of savings, emphasizing the impact of both physical and control-based interventions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“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
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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