In a commercial building, a significant amount of energy is used by the ventilation systems to condition the air for the indoor environments to satisfy the required quantity (temperature and humidity) and quality (amount of fresh air). For many years, Variable Air Volume (VAV) systems have been considered as the most efficient solutions by balancing the airflow volume based on the demand making them energy efficient when compared with the traditional Constant Air Volume (CAV) systems. However, the setpoints in VAV systems are often misread by the sensors due to stratification and formation of pollutant pockets and responding to design levels that overestimate the real-time demand conditions, which result in waste of energy, thermal discomfort and unhealthy air. In general, VAV devices are expensive, complicated and prone to failures and they are used only in medium and large projects. More recently, new technologies have evolved to solve this issue. In one of the new solutions, VAV motors terminals are replaced with flaps which are simpler and less expensive thus, they can be implemented in a wider range of projects. In systems, balancing and supplying the optimal airflow to reduce the energy consumption while delivering ideal thermal and Indoor Air Quality (IAQ) levels are the main challenges. In this paper, a comparison of the recent technologies with traditional VAV systems is presented to be used as a guild line for researchers and designers in the field of Heating Ventilation Air Conditioning (HVAC).
A critical gap between the occupant behaviour research field and the building engineering practice limits the integration of occupant-centric strategies into simulation-aided building design and operation. Closing this gap would contribute to the implementation of strategies that improve the occupants’ well-being while reducing the buildings’ environmental footprint. In this view, it is urgent to develop guidelines, standardised methods, and supporting tools that facilitate the integration of advanced occupant behaviour models into the simulation studies. One important step that needs to be fully integrated into the simulation workflow is the identification of influential and non-influential occupant behaviour aspects for a given simulation problem. Accordingly, this article advances and demonstrates the application of the Impact Indices method, a fast and efficient method for screening the potential impact of occupant behaviour on the heating and cooling demand. Specifically, the method now allows the calculation of Impact Indices quantifying the sensitivity of building energy use to occupancy, lighting use, plug-load appliances use, and blind operation at any spatial and temporal resolution. Hence, users can apply it in more detailed heating and cooling scenarios without losing information. Furthermore, they can identify which components in building design and operation require more sophisticated occupant behaviour models. An office building is used as a real case study to illustrate the application of the method and asses its performance against a one-factor-at-a-time sensitivity analysis. The Impact Indices method indicates that occupancy, lighting use and plug-load appliances have the greatest impact on the annual cooling demand of the studied office building; blind operation is influential only in the west and south façades of the building. Finally, potential applications of the method in building design and operation practice are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.