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This paper details the methodologies for creating and training grey-box thermal models for low-rise residential buildings. This paper covers different aspects of model development such as model vectorization, cost function definition and parameter estimation. Different computing strategies such as GPU accelerated calculation are also explored. This paper also briefly demonstrates the possibility of rearranging the model equation for predictive control purposes. The performance of an example grey-box model is tested against common datadriven machine learning models using a dataset of over 500 dwellings. Overall the grey-box model achieved good temperature prediction accuracy at a 4-hour forecast horizon and produced meaningful insights from the estimated parameters. The possibilities of using grey-box models for more advanced applications, such as model predictive control and remote auditing are also discussed.

On Formulation and Training of Grey-box Thermal Model for Low-rise Residential Buildings