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Auflistung nach Schlagwort "Energy Efficiency"

1 - 5 von 5
  • Konferenzbeitrag
    Artificial Intelligence-Based Assistance Systems for Environmental Sustainability in Smart Homes: A Systematic Literature Review on Requirements and Future Directions
    (INFORMATIK 2024, 2024) Brîncoveanu, Constantin; Carl, K. Valerie; Binz, Simon; Weiher, Moritz-Andre; Thomas, Oliver; Hinz, Oliver
    Artificial Intelligence (AI) is increasingly being utilized to promote sustainable behavior, particularly in the context of smart homes. Such solutions can significantly enhance resource consumption sustainability by leveraging data analysis for ecological benefits. This systematic literature review examines the requirements for data-driven AI applications aimed at improving environmental sustainability in smart homes, based on an analysis of 60 selected papers. Key findings include the importance of predictive analytics, privacy and security, context-aware features, real-time monitoring, interoperability, efficiency strategies, personalized user engagement, user interface design, and behavioral aspects. We highlight technological advancements that facilitate more comprehensive applications and identify the need for integrating diverse features to build consumer trust and acceptance. This review provides an overview of current smart home technologies and suggests future research directions to enhance energy efficiency, user comfort, and environmental sustainability.
  • Textdokument
    Energieeffizientes Kaltstartverhalten spanender Werkzeugmaschinen
    (INFORMATIK 2021, 2021) Walz, Deborah; Wächter, Andreas; Tomov, Stefan; Heimbach, Konrad; Weigold, Matthias
    Die Kompensation thermischer Einflüsse und daraus resultierender geometrischer Verlagerungen spielt eine bedeutende Rolle bei der Gewährleistung einer hohen Bearbeitungsqualität von Werkstücken in Zerspanungsprozessen. Übliche Vorgehensweisen zur Reduktion thermischer Verlagerungen während der Produktion gehen mit einem erheblichen Energiebedarf einher oder modellieren die komplexen Zusammenhänge thermischer Einflüsse nur ungenügend. Methoden des Maschinellen Lernens stellen einen vielversprechenden Ansatz zur Modellierung dar. Es wird eine Lösung angestrebt, die aufwandsarm auf Produktionsmaschinen ähnlicher Bauart übertragen werden kann. Derzeit ist ungeklärt, ob eine explizite oder implizite Modellierung der zeitlich multivarianten Daten eine ufriedenstellende Lösung bietet. Als besonders herausfordernd stellt sich die Verfügbarkeit von ausreichend vielen Datenbeispielen zur Modellierung der relevanten Größen dar.
  • Konferenzbeitrag
    Energy and resource comparison of current applications with a focus on statistical analyses and evaluations using the example of MATLAB and R
    (EnviroInfo 2023, 2023) Seegert, Tim; Bergmann, Malina; Brömme, Josephine; Junger, Dennis; Wohlgemuth, Volker
    This paper compares the energy and resource efficiency between MATLAB and R, two widely used programming languages in scientific computing and data analysis. A load driver and automation software, Power Automate, were utilized as a system under test to measure and evaluate the performance of both languages. Before the experiment, specific mathematical operations and execution methods were developed in MATLAB and R scripts. The measurement and evaluation were conducted using the Oscar framework. The results indicate that R outperforms MATLAB in baseline and statistical operations, while MATLAB excels in matrix calculations. These findings provide valuable insights for selecting the most suitable programming language based on specific computational requirements, optimizing energy consumption and resource utilization.
  • Konferenzbeitrag
    Towards Sustainable Machine Learning: Analyzing Energy-Efficient Algorithmic Strategies for Environmental Sensor Data
    (INFORMATIK 2024, 2024) Cetkin, Berkay; Begic Fazlic, Lejla; Guldner, Achim; Naumann, Stefan; Dartmann, Guido
    This study evaluates the energy efficiency of machine learning (ML) classification models across 49 test setups, each representing different conditions derived from a set of scenarios. Utilizing internet of things (IoT) technology with an ESP8266 microcontroller, we collected and analyzed environmental data including temperature, humidity, and CO2 levels from a simulated room environment. We measured energy consumption for data preprocessing, model training, and testing, alongside energy efficiency metrics that consider output, processing time, and F1 score. The study also performed correlation analyses to explore the relationship between energy consumption and performance metrics. Furthermore, it assessed the trade-offs between accuracy and energy efficiency by comparing an ensemble model to its constituent algorithms. The measurements, conducted according to the Green Software Measurement Model (GSMM), provide essential insights into selecting energy-efficient algorithms for a broad spectrum of IoT applications.
  • Textdokument
    VSC-3 – a summary of the experiences with an HPC system immersed in oil
    (INFORMATIK 2021, 2021) Zabloudil, Jan; Haunschmid, Ernst
    The electric energy consumed by computer systems is fully converted into heat, which needs to be removed from the system. As the internal temperature of processor chips is typically 80°C in operation and the temperature of the outside air hardly exceeds 45°C, a physicist would not easily understand, why heat pumps, consuming extra energy, are needed to remove heat to the outside. Traditional computer centers nevertheless consume copious amounts of extra electric energy to operate heat pumps. Consequently, various options for the efficient cooling of computers have been considered for several decades. Immersion cooling is an intriguingly simple option. You just find the right fluid and submerge the whole system in it. Then you only cool the fluid, which should be simple. There are, however, challenges to be met, which will be discussed. After several years of experiments with a small system, VSC-3 was procured as a full-scale immersion cooled system. Already at installation, several issues needed to be addressed, mostly concerning the compatibility of various submerged components with the fluid. Nevertheless, with some adaptions, the system got up and running. Due to the very simple hardware surrounding the nodes, the system had a very good price/performance ratio and, from a point of view of energy efficiency, the whole installation surpassed the expectations with an average PoE of 1.03. Additionally, roughly 10% of the energy consumption of a conventional system were saved by the absence of cooling fans. There were, however, serious downsides. First and foremost, the maintenance of VSC-3 was challenging to say the least. While compute nodes had hardly any problem, many other components suffered from the prolonged contact with hot fluid. Cables became stiff and some wicked oil outside the containers. Legions of InfiniBand switches failed for reasons, that will probably never be fully understood. Also, the containers and the oil installation were prototypes, leading to leakage. Ultimately, the question of the future of immersion cooled systems remains. Let’s assume, engineering problems can be solved without destroying the economic appeal of immersion cooling. The energy efficiency aspect will become more important in a greener future. The main problem, which remains to be solved, is the compatibility of computing components with the immersion fluid. Looking at VSC-3 as our main system for several years, we must however say, it served us very well. During it’s 6.5 years it delivered more than 1.25 billion core-hours to more than 700 projects.