Feature assessment frameworks to evaluate reduced-order grey-box building energy models

With a drive towards achieving an integrated energy system, there is a need for holistic and scalable building modelling approaches for the commercial building stock. Existing grey-box modelling approaches often fail to produce a generalised network structure, which limits the suitability of models for different applications. Furthermore, existing feature assessment frameworks provide limited opportunities to quantify the potential of model characteristics in terms of flexibility, scalability and interoperability. Considering the diversity of the possible characterisation approaches, this study aims to define and assess a set of basic and derived features for reduced-order grey-box models through a generalisable framework that would act as a decision support tool for the identification of appropriate model characteristics. This research proposes an integrated methodology to test and evaluate model features, namely, scalability, flexibility, and interoperability for reduced-order grey-box models and formulates test-cases with the available commercial reference buildings published by the Department of Energy of the United States. The model scalability errors lie between 3.42% and 4.35% that indicates the suitability of implementing a zone level model for model predictions at the whole building level. The model flexibility error decreased from 5.73% to 4.78% when considering a trade-off between accuracy and complexity. These frameworks produce scalable and flexible models that facilitate urban energy modelling of building stocks and subsequent evaluation of retrofit strategies. Furthermore, the devised models aid the implementation of heat demand reduction scenarios in a building cluster to achieve an integrated energy system.