Surrogate models are an important part of building energy labelling programs, but so far these models 11 still present low accuracy, particularly in cooling dominated climates. The objective of this study is to 12 evaluate the feasibility of using artificial neural network (ANN) to improve the accuracy of surrogate 13 models for labelling purposes. ANN is applied to model the building stock of a city in Brazil, based on 14 the results of extensive simulations using the high-resolution building energy simulation program 15 EnergyPlus. Sensitivity and uncertainty analysis were carried out to evaluate the behaviour of the ANN 16 model, and the variations in best and worst performance for several typologies were analysed in face of 17 variation in input parameters and building characteristics. Results indicate that ANN can represent the 18 interaction between input and output data in a vast and diverse building stock; with errors of ± 16% for a 19 confidence level of 90% of the cases. Sensitivity analysis shows that no single input parameter can be 20 identified as the main responsible for the building energy performance. Uncertainty in building usage 21 plays a major role in the building energy performance, as well as the facade area and the shell-to-floor. 22 Results of this work may have a profound impact as ANN may be applied in the future in the regulation 23 of many other countries, with further impact in the energy consumption and life quality of large amounts 24 of people. 25 26 Keywords: artificial neural network, building energy simulation, surrogate model, low-resolution 27 model, regulation, labelling, Latin Hypercube method. 28 29 Melo, A.P., Cóstola, D., Lamberts, R. & Hensen, J.L.M. 2014. "Development of surrogate models using artificial neural network for building shell energy labelling", Energy Policy, vol. 69,
Melo et al. REF use ANN to improve the accuracy of surrogate models for labeling purposes, based on simulations results.
Development of surrogate models using artificial neural network for building shell energy labelling
Energy Policy
Engineering