arXiv subjects

Gerald Schweiger

Publications and source records attributed to Gerald Schweiger.

12 recordsLinked to original sources

arXiv2026

Type Checking Project Haystack Grids using JSON Schema and Pydantic

Ontologies enable scalable energy services in buildings by supporting interoperability and automation. Project Haystack is a building ontology that is widely adopted due to its flexible, tag-based semantic model, openness, and extensibility, but suffers from ambiguous tag usage and limited automated validation. Although Project Haystack is formally open, its reliance on custom file formats and domain-specific languages that originate from the Haxall ecosystem creates a de facto barrier to integration. In this paper, we address these limitations by introducing a Python-based toolchain for Haystack. We present (i) a parser for Haystack definition files (Trio file format), and (ii) a code generator that derives Pydantic models and JSON Schema definitions from these parsed specifications. The resulting models enable static type checking and enable structural validation of Haystack grids within Python, as well as schema-based validation of JSON representations outside the Python ecosystem. All tools, generated models, and schemas are released publicly under an open-source license, with the goal of strengthening the Haystack ecosystem and opening a practical pathway beyond its current technical boundaries.

cs.PL↗

arXiv2025

Institutional cooperations in Austrian research: An analysis of shared researchers

Multiple organisational affiliations are an increasingly common feature of research systems, yet their implications for organisational performance had received limited systematic attention. We developed a scalable, network-based analytical framework that represents simultaneous researcher affiliations as relational links between organisations and applied it to bibliometric data from Austria. Using harmonised publication and affiliation metadata, we constructed two complementary co-affiliation networks: a complete network capturing all simultaneous affiliations and a temporally filtered network retaining only organisational pairs that recurred over time. Network regression analyses showed that geographical proximity remained an important determinant of co-affiliation formation, with spatial distance consistently reducing shared appointments. Clear sectoral differences emerged beyond geography. Universities formed a dense and persistent core of co-affiliations, whereas ties involving medical institutions, government, non-profit and private-sector organisations were often short-lived and attenuated under temporal filtering. Among crosssector links, co-affiliations between universities and research institutes were notably resilient, indicating a more structurally embedded form of organisational integration. We assessed the effect of concurrent affiliations on organisational citation impact across organisational types using field- and year-normalised indicators. Research institutes and universities consistently exhibited higher citation impact than organisations from other sectors, and persistent co-affiliations were associated with greater and more stable scientific visibility.

cs.DL↗

arXiv2025

Reforming research funding: Combining editorial preregistration with grant peer review

Competitive grant funding is associated with high costs and a potential bias to favor conservative research. This comment proposes integrating editorial preregistration, in the form of registered reports, into grant peer review processes as a reform strategy. Linking funding decisions to in principle accepted study protocols would reduce reviewer burden, strengthen methodological rigor, and provide an institutional foundation for (more) replication, theory driven research, and high risk research. Our proposal also minimizes strategic proposal writing and ensures scholarly output through the publication of preregistered protocols, regardless of funding outcomes. Possible implementation models include direct coupling of journal acceptance with funding, co review mechanisms, voucher systems, and lotteries. While challenges remain in aligning journal and funding agency procedures, the integration of preregistration and funding offers a promising pathway toward a more transparent and efficient research ecosystem.

cs.DL↗

arXiv2025

Reproducibility of Machine Learning-Based Fault Detection and Diagnosis for HVAC Systems in Buildings: An Empirical Study

Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable results sparked ongoing discussions about research practices. In recent years, the fast-growing field of Machine Learning (ML) has become part of this discourse, as it faces similar concerns about transparency and reliability. Some reproducibility issues in ML research are shared with other fields, such as limited access to data and missing methodological details. In addition, ML introduces specific challenges, including inherent nondeterminism and computational constraints. While reproducibility issues are increasingly recognized by the ML community and its major conferences, less is known about how these challenges manifest in applied disciplines. This paper contributes to closing this gap by analyzing the transparency and reproducibility standards of ML applications in building energy systems. The results indicate that nearly all articles are not reproducible due to insufficient disclosure across key dimensions of reproducibility. 72% of the articles do not specify whether the dataset used is public, proprietary, or commercially available. Only two papers share a link to their code - one of which was broken. Two-thirds of the publications were authored exclusively by academic researchers, yet no significant differences in reproducibility were observed compared to publications with industry-affiliated authors. These findings highlight the need for targeted interventions, including reproducibility guidelines, training for researchers, and policies by journals and conferences that promote transparency and reproducibility.

cs.LG↗

arXiv2024

Project Beyond: An Escape Room Game in Virtual Reality to Teach Building Energy Simulations

In recent years, Virtual Reality (VR) has found its way into different fields besides pure entertainment. One of the topics that can benefit from the immersive experience of VR is education. Furthermore, using game-based approaches in education can increase user motivation and engagement. Accordingly, in this paper, we designed and developed an immersive escape room game in VR to teach building energy simulation topics. In the game, players must solve puzzles like, for instance, assembling walls using different materials. We use a player guidance system that combines educational content, puzzles, and different types of hints to educate the players about parameters that influence energy efficiency, structural resistance, and costs. To improve user onboarding, we implemented a tutorial level to teach players general interactions and locomotion. To assess the user experience, we evaluate both the tutorial and the game with an expert study with gaming and VR experts (n=11). The participants were asked to play both the tutorial level and the escape room level and complete two sets of post-questionnaires, one after the tutorial and one after the puzzle level. The one after the tutorial level consisted of NASA-TLX and SUS questionnaires, while after the escape room level we asked users to complete the NASA-TLX, UESSF, and PXI questionnaires. The results indicate that the onboarding level successfully provided good usability while maintaining a low task load. On the other hand, the escape room level can provide an engaging, visually appealing, and usable learning environment by arousing players' curiosity through the gameplay. This environment can be extended in future development stages with different educational contents from various fields.

cs.HC↗

arXiv2024

Generalizable Temperature Nowcasting with Physics-Constrained RNNs for Predictive Maintenance of Wind Turbine Components

Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications.

cs.LG↗

arXiv2024

The Costs of Competition in Distributing Scarce Research Funds

Research funding systems are not isolated systems - they are embedded in a larger scientific system with an enormous influence on the system. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does competition for funds affect doing risky research? How do competitive funding environments affect scientists themselves, and which ethical issues must be considered? We attempt to identify gaps in our knowledge of research funding systems; we propose recommendations for policymakers and funding agencies, including empirical experiments of decision processes and the collection of data on these processes. With our recommendations we hope to contribute to developing improved ways of organizing research funding.

econ.GN↗

arXiv2024

Introducing the comfort performance gap in new educational buildings: a case study

Providing adequate indoor environmental quality is crucial in educational settings. In this paper, we implemented and tested a framework that collects occupant feedback and investigated correlations between teachers comfort and the operational characteristics of an Austrian school building in September and October 2022. Initial results show that the measured average temperatures (23.1 t-25.1 deg C) in all rooms are on the upper limit of various recommendations, such as comfort guidelines for building operation or workplace regulations. This assessment is in line with the feedback we received from the teachers. A literature review demonstrated that childrens comfort temperatures are lower compared to adults. Hence, it is reasonable to conclude that indoor temperatures during the survey period were inadequate for the pupils either, even without direct feedback. An analysis of the CO2 measurements showed that, during school hours, approximately 20% of all measurement values were above 1000 ppm, with 2% above 1500 ppm. CO2 levels above 1000 ppm are considered hygienically critical, with the latest research proposing to lower the limits below 800 ppm to ensure a healthy and effective learning environment. While we only assessed the challenges of providing a healthy indoor environment for an educational building in Austria, our literature review shows similar challenges and research efforts worldwide. Our analysis demonstrates the need for adapting design requirements, especially for school buildings, acknowledging the different comfort needs of adults and children and the importance of high indoor air quality for providing an optimum learning environment. Future research should focus on testing adapted indoor environmental quality requirements for schools, especially in urban areas, and how to integrate real-time occupant feedback in the heating, ventilation and air conditioning systems.

physics.soc-ph↗

arXiv2023

\"Uber wissenschaftliche Exzellenz und Wettbewerb

The pursuit of excellence seems to be the True North of academia. What is meant by excellence? Can excellence be measured? This article discusses the concept of excellence in the context of research and competition.

econ.GN↗

arXiv2022

Constructing Neural Network-Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in data-driven modeling techniques, in particular neural networks have proven to provide an effective framework for solving a wide range of tasks. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

cs.LG↗

arXiv2019

Modeling and simulation of large-scale Systems: a systematic comparison of modeling paradigms

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper complements existing surveys on large-scale modeling and simulation of physical systems by conducting expert surveys. We conducted a two-stage empirical survey in order to investigate research needs, current challenges as well as promising modeling and simulation paradigms. Furthermore, we applied the analytic hierarchy process method to prioritise the strengths and weakness of different modeling paradigms. The results of this study show that experts consider acausal modeling techniques to be suitable for modeling large scale systems, while causal techniques are considered less suitable.

math.NA↗

arXiv2019

An Empirical Survey on Co-simulation: Promising Standards, Challenges and Research Needs

Co-simulation is a promising approach for the modelling and simulation of complex systems, that makes use of mature simulation tools in the respective domains. It has been applied in wildly different domains, oftentimes without a comprehensive study of the impact to the simulation results. As a consequence, over the recent years, researchers have set out to understand the essential challenges arising from the application of this technique. This paper complements the existing surveys in that the social and empirical aspects were addressed. More than 50 experts participated in a two-stage Delphi study to determine current challenges, research needs and promising standards and tools. Furthermore, an analysis of the strengths, weakness, opportunities and threats of co-simulation utilizing the analytic hierarchy process resulting in a SWOT-AHP analysis is presented. The empirical results of this study show that experts consider the FMI standard to be the most promising standard for continuous time, discrete event and hybrid co-simulation. The results of the SWOT-AHP analysis indicate that factors related to strengths and opportunities predominate.

cs.CY↗