Calibration of detailed building energy simulation models to measured data using uncertainty analysis
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Over the past few decades, advances in technology, most notably the industrial revolution of the late 18th-century, has brought about dramatic improvements in the socioeconomic circumstances of developed nations. This has also brought with it rapid change in terms of human population, environmental impacts as well as energy consumption. Growth in energy consumption has been largely associated with increased use of finite fossil fuels (oil, coal, gas) in industrialized nations. However, this growth is unsustainable due to the depletion of these natural resources as well as the impact their consumption has on the environment, in terms of carbon dioxide (CO2) emissions. A shift towards renewable fuels (wind, hydro, solar, geothermal, tidal) is currently underway, but progress remains slow, and the current reliance on fossil fuels for many existing essential technologies (e.g. transport) remains a major barrier to the large-scale transition that is required. Energy efficiency has the potential to mitigate greenhouse gas emissions (GHG) and provide additional scope for the transition to a sustainable renewables-based energy future. Buildings account for approximately 40% of global energy consumption. Approximately half of this energy requirement stems from space heating and cooling. Studies have shown that savings of up to 40% are possible through the implementation of energy conservation measures (ECM's) and continuous commissioning (CC). Whole building energy simulation tools have the potential to play a significant role in achieving this goal. However, their widespread adoption in the AEC (Architecture, Engineering and Construction) industry depends on their perceived reliability and the accuracy of their outputs. Currently, simulation tools are used primarily in building design with little integration or comparison to real building operation. It is often found that the actual buildings perform far worse than the design simulation initially predicted. This gap between measured and simulated data needs to be carefully addressed. This thesis proposes a new methodology for calibrating building energy simulation (BES) models to measured data including the incorporation of parameter uncertainty into final model predictions and recommendations.
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