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Statistical regression models were developed to permit the rapid modelling of large commercial office buildings within a single climate zone. The regression models are developed using a large number of building parameters and their hourly energy model simulated results. In previous building energy regression modelling, there is a research gap in selecting building parameters using statistical approaches. This paper investigates a feature selection method, including forward stepwise selection and LASSO regression, to identify building parameters that, together, have the most significant impact on total building energy load. The regression model, with 25 features selected through this methodology, predicts total energy load at 93.5% accuracy, on average.

Building Energy Use Surrogate Model Feature Selection – A Methodology Using Forward Stepwise Selection and LASSO Regression Methods