Building energy model calibration using a surrogate neural network

Abstract

Energy studies of buildings are becoming more widespread as stakeholders strive to improve energy efficiency and reduce carbon emissions. As a result, there is an increased need for novel numerical techniques to automatically calibrate building energy models (BEMs) for these energy studies. In this paper, a new automated building calibration methodology was developed, which uses a surrogate (i.e., meta) multilayer perceptron artificial neural network (ANN) to infer unknown building parameters. Typically, in the field of BEM calibration, surrogate models are used as a time-saving technique. Instead of running the building simulation software during each iteration of an optimization search, a surrogate model can approximate the output of the BEM very quickly, leading to much faster optimization of the unknown building parameters. However, we show that, once trained, the surrogate model itself can be used to find the unknown building parameters. Since the surrogate model is differentiable, gradient descent can be used to find the building parameters that minimize the error between true and predicted metered energy consumption. Previous surrogate modeling approaches for calibrating BEMs map only the unknown building parameters to the BEM’s output. As a result, they are blind to the effects of other crucial variables like weather and the building’s schedules. On the other hand, our surrogate ANN model accounts for these other predictors of energy consumption. With this advantage, our method was able to outperform a powerful black box optimizer when finding 14 unknown building parameter values in a controlled case study with hourly energy data. In a real metered data case study, our ANN method and the black box optimizer performed similarly on average, but our method had less variance in performance across trials.