Hall, Monika

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Hall
Vorname
Monika
Name
Hall, Monika

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20232
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Einstellungen

Autor:in: Institut Nachhaltigkeit und Energie am Bau, Hochschule für Architektur, Bau und Geomatik FHNW × Thema: 624 - Ingenieurbau und Umwelttechnik ×

Suchergebnisse

Gerade angezeigt 1 - 2 von 2
  • Vorschaubild
    Publikation
    Gravity ventilation for interior bathrooms
    (IOP Publishing, 01.12.2023) Hall, Monika; Gerber, Vincent; Geissler, Achim [in: Journal of Physics: Conference Series]
    Based on the extensive experience of a building cooperative with interior bathroom gravity (shaft) ventilation in existing apartment buildings, the replacement buildings constructed at the same location are also equipped with gravity ventilation. The aim of the project described here is to demonstrate the possibilities and limitations of gravity ventilation in one of the new replacement buildings by means of monitoring. Detailed monitoring over a complete year recorded the behaviour and effectiveness of the gravity ventilation in all seasons. In winter, gravity ventilation leads to higher air change rates in interior bathrooms than in summer. In general, humidity can be removed with gravity ventilation except in summer, when after a shower the bathroom door stays closed for 24 h. In summer when the indoor and ambient temperature is the same the gravity ventilation does not work. In this case, the interior bathroom should be ventilated by the main apartment ventilation, e.g., while the bathroom door and other room doors and windows are open at the same time. In summer, doors and windows are often open and the gravity ventilation summer problem can be viewed as negligible. Therefore, gravity ventilation is a good alternative to other ventilation systems in interior bathrooms.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Determine the heat demand of existing buildings with machine learning
    (IOP Publishing, 01.12.2023) Hofmann, Joachim Werner; Amoser, Christian; Geissler, Achim; Hall, Monika [in: Journal of Physics: Conference Series]
    The renovation rate of existing buildings plays a major role in the Swiss Energy Strategy 2050+. To increase this rate, there must be a simple and cost-effective method to determine the heat demand of existing buildings. In this paper, the generation of such a method, based on the Swiss cantonal building energy certificate (GEAK) database with the help of machine learning (ML), is studied. The aim of the project was to develop a ML model which allows the heat demand of existing buildings to be determined quickly with a minimal set of parameters. The comparison of the GEAK building envelope class for single family houses calculated with the new ML model and the original GEAK classes shows that approximately 62 % have the same class, 32 % differ by one class and 6 % by two classes. The ML model is a good starting point for further refinements and developments.
    01A - Beitrag in wissenschaftlicher Zeitschrift