A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction

D. Hou and R. Evins

Renewable and Sustainable Energy Reviews, 2024, vol. 193, issue C

Abstract: Because of their low computational costs, surrogate models (SMs), also known as meta-models, have attracted attention as simplified approximations of detailed simulations. Besides conventional statistical approaches, machine-learning techniques, such as neural networks (NNs), have been used to develop surrogate models. However, surrogate models based on NNs are currently not developed in a consistent manner. The development process of the models is not adequately described in most studies. There may be some doubt regarding the abilities of such models due to a lack of documented validation. In order to address these issues, this paper presents a protocol for the systematic development of NN-based surrogate models and how the procedure should be reported and justified. The protocol covers the model development procedure sample generation, data processing, SM training and validation, how to report the implementation, and how to justify the modeling choices.

Keywords: Protocol; Surrogate model; Meta-model; Neural networks; Building energy; Synthetic data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114283

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