Smart building energy management system for improved energy efficiency

Abstract

A thermal analysis was selected as a dissertation topic due to insufficient data from published scientific journals on detailed glazing thermal performance in parametric façades. Three glazing types, NC52, NC60E and NC55E, were analysed for this application. In South Africa, HVAC engineers' work is limited to professional fees, thus, the opportunity to value engineer an architectural design through thermal analysis is not always available. This dissertation clearly explains why thermal modelling should be adopted in every façade building design. The Capitec head office building engineering team was required to perform a thermal analysis of the building at an early design stage. The main aim was to inform the architectural team how a parametric façade could be optimally designed to reduce the internal cooling requirement below a design threshold of 180W/m2. A thermal comfort study was thus done to investigate the thermal performance following the GreenStar standard and SANS regulations. The software DesignBuilder™ with EnergyPlus™ was used to evaluate four glazing types; clear glazing as a baseline, NC60E, NC55E and NC52 performance glazing. The analysis provides detailed information on thermal performance for total cooling loads, internal and external solar heat gains, and various psychrometric values such as temperatures and humidity. The thermal comfort study aims to identify the Fanger predicted mean vote and discomfort hours, summarised as a weighted average for the best and worst-performing zones. The thermal analysis showed that clear glazing is not practical for a façade and can result in 40% higher internal heat gains. Low emissivity glazing is high-performance glazing, and the application needs to be justified against the higher cost over a less expensive performance glazing. NC52 glazing can be significantly improved through energy-efficient passive architecture such as parametric facade design combined with external shading. Optimisation of the roof heat transfer through a green roof was done via a thermal heat transfer coefficient which showed no significant energy savings compared to a well-optimised building. Thermal comfort was measured in each zone as a weighted average. It showed insufficient thermal acceptance levels in most zones as the dissatisfaction percentage was too high. Further data optimisation requires the application of computational fluid dynamics, data-driven models analysis, zone control and spatial distribution in multi-zoned areas, airflow optimisation and night ventilation. In addition to this, only measurement and verification can genuinely confirm the accuracy of a predictive simulation model.