Energy consumption in buildings contributes to 41% of global carbon dioxide emissions through electricity and heat production, making the design of mechanical systems in buildings of paramount importance. Industry practice for design of mechanical systems is currently limited in the conceptual design phase, often leading to sub-optimal designs. By using Generative Design (GD), many design options can be created, optimized and evaluated, based on system energy consumption and life-cycle cost (LCC). By combining GD for Architecture with GD for HVAC, two areas of building design can be analyzed and optimized simultaneously, resulting in novel designs with improved energy performance. This paper presents GD for HVAC, a Matlab script developed to create improved zone level mechanical systems for improved energy efficiency. Through experiments, GD methodologies are explored and their applicability and effect on building HVAC design is evaluated.
Heating, ventilation and air-conditioning (HVAC) systems account for a significant portion of energy consumption in buildings. The majority of fault detection research has neglected zone-level faults. In this study, the methodology of a quantitative model-based fault detection and diagnostics (FDD) system for the zone-level is presented. The creation of a basic model was completed using Matlab. Analyses were conducted, identifying five zone-level inefficiencies. The severity of these inefficiencies was analyzed and an excessive amount of air handling unit (AHU) fan energy consumption was detected. The redundancy of these faults in the building force the AHU to expend an unwarranted amount of energy, highlighting the importance of utilizing FDD. Benefits of this methodology include the detection of pre-existing faults originating from the initial design and the ability to apply this to new or existing buildings, improving the efficiency of the building.ii
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