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Item type:Publication, Access status: with files , Personalized low-cost thermal comfort monitoring using IoT technologiesThermal comfort plays an essential role in the well-being and productivity of occupants. Typically, thermal comfort is assessed either through surveys completed by building occupants or through sensor data that is analyzed using thermal comfort models. Automating comfort surveys and data collection processes reduce the risk of information loss, providing more accurate and personalized thermal comfort assessments over longer periods of time. To this end, this paper presents the design and implementation of a thermal comfort monitoring system consisting of low-cost hardware components and using IoT technologies. The system consists of intelligent wireless sensor nodes that collect and process environmental data, a portable main station that integrates and stores data, and a digital survey that provides feedback from building occupants. To ensure accuracy, the low-cost hardware components of the intelligent sensor nodes are calibrated in a climate chamber, using high-precision sensors for reference. After calibration, the system is deployed in a field test where several intelligent sensor nodes collect environmental data in an office, while occupants complete the digital thermal comfort survey. In addition, thermal comfort indexes are computed by the intelligent sensor nodes and compared with the feedback of each building occupant. The results indicate that the low-cost thermal comfort monitoring system successfully collects and integrates thermal comfort data from the intelligent sensor nodes and the digital survey, being able to create personalized thermal comfort profiles. In future work, the system can be used in large-scale thermal comfort surveys, to develop personalized thermal comfort models and to control personalized comfort systems.Publicationtype: Journal Article10 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Transforming science journalism through collaborative research : a case study of the German “WPK Innovation Fund for Science Journalism”(SISSA, 2024-03-25); ; ; ; Science journalism, a unique form of science communication, faces grand challenges requiring innovation for its sustainability. This practice insight delves into a research-practice collaboration addressing the “WPK Innovation Fund for Science Journalism”, a pioneering support infrastructure for innovation in German science journalism. Our transformative accompanying research project aims to both support the fund’s development as well as advance science journalism research. This report, co-authored by researchers and practitioners, showcases opportunities and challenges, drawing from the three forms of knowledge generated in the collaboration: systems, target, and transformation knowledge. Each of these forms sheds light on specific lessons learned in our project on how to conduct transformative journalism research.Publicationtype: Journal Article43 124 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Anwendung künstlicher neuronaler Netze zur Bestimmung von U-Werten(2024-03); The in-situ estimation of U-values is usually associated with high inaccuracies and long measuring campaigns. Literature sources mention time intervalls of 72 hours up to more than two weeks as well as derivations of up to 40 % (Evangelisti et al., 2016). Main reasons cited for these circumstances are high thermal capacities, low temperature gradients, transient boundary conditions and alternating directions of the heat flow. Artificial neural networks (ANNs) offer the possibility of quantifying the correlations listed above. By choosing a regression-based approach implemented with ANNs, transient environmental influences (e.g., heat flow, air temperature and solar radiation) can be correlated with variables that are by definition stationary, such as the U-value. In this paper, the authors present the application of ANNs for estimating U-values using simulation data. The training data is generated exclusively by transient simulations of building elements based on finite elements, eliminating the need for extensive measurements to generate the training data. In this work, three-layer neural networks with 2 to 20 neurons within the hidden layer are used for the regression of U-values. The regression results obtained are compared with U-values of the mean value method for referencing the proposed methodology.Publicationtype: Conference Paper8 26