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The present study describes the development of a multi-linear regression model to predict the effect of building shape on total energy consumption in two different climate regions (i.e. cold-dry and warm-marine). Seven building shapes including H-shape, T-shape, rectangle, etc. were considered in this study. The simplified model can be used to conduct a parametric study in order to investigate the effect of building parameters on total heating and cooling load. Building simulation software programs, including eQUEST and DOE-2 were used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousand simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. Statistical analysis was performed using R statistical analysis program to develop a set of linear regression equations predicting energy consumption of each design scenario. In addition, the influence of several design parameters on building energy consumption was further investigated using the sensitivity analysis procedure. The difference between regression-predicted and DOE-2 simulated annual building energy consumption were largely within 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered. [ABSTRACT FROM AUTHOR]

Modeling of energy consumption in buildings is essential for different applications such as building energy management and establishing baselines. This makes building energy consumption estimation as a key tool to reduce energy consumption and emissions. Energy performance of building is complex, since it depends on several parameters related to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper presents a new model to predict and quantify energy consumption in commercial buildings in the early stages of building design. Building simulation software including eQUEST and DOE-2 was used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousands simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. The present study considered building materials, their thickness, building shape, and occupant schedule as design variables since building energy performance is sensitive to these variables. Then, the results of the energy simulations were implemented into a set of regression equation to predict the energy consumption in each design scenario. A good agreement was seen between the predicted data based on the developed regression model and DOE simulation and the maximum error was less than 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered. [ABSTRACT FROM AUTHOR]