The focused investigation of building design is necessary to understand and quantify the implication of different design parameters on their energy performance. The design of future buildings is a major challenge, as current designs may be inappropriate in a future with global warming due to climate change impacts. In addition this understanding is necessary to be able to predict timing and profile of future energy demand, which is crucial for the long-term planning of energy infrastructures – particularly electricity. In this paper, the Morris Elementary Effects method is used as a screening method, to identify the key parameters of the design and operation of office buildings that affect the estimation of space cooling peak load and annual energy demand. Internal heat gains, cooling set-point and ventilation rates are identified as the parameters with larger implications for both annual and peak space cooling demand. In future climate scenarios, the magnitude of change of annual space cooling demand is significantly (around five times) larger than the change in the peak demand. Asides from the potential increase of space cooling demand in future scenarios, the sensitivity of the space cooling demand relative to the change in design parameters is potentially much larger.
The increase of thermal discomfort and the associated energy demand for the cooling of buildings further exacerbate the ongoing challenges for the design of new buildings and the adaptation measures of the current building stock to climate change. Dynamic, detailed and tailored building simulation methodologies are necessary to better understand and quantify the energy demand of buildings and consequences for supporting energy networks. The aim of this is to analyze the sensitivity of peak electricity demand for the provision of space cooling to different office building model parameters. In the research, two sensitivity methods, Morris Elementary Effect and Sobol, are used to evaluate the sensitivity of eight input model parameters. Relative to peak power consumption for cooling, the most important factor is the cooling set-point, followed by ventilation rate and internal heat gains, which are estimated to account for 38%, 26% and 25% of the variation. Regarding the annual cooling demand for the HVAC systems, these factors account for 35%, 33% and 58%, respectively.
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