This paper presents thermal resistance-capacitance (RC) network models developed from the parametric simulations of building archetypes for early design stage energy demand assessment. Building energy performance is typically measured using physics-based simulation tools that require expert knowledge, high computational cost and detailed building information that are unavailable at the early design stage. These barriers make the white-box models inefficient for multi-scenario analyses of near-optimal building control and operation strategies. The thermal RC networks are typical grey-box models that use simplified physical descriptions to simulate a building’s thermal behaviour while retaining a state-space formulation that can be efficiently solved for different parametric simulations and optimization. Previous publications have explored the comparative performance of physics-based and data-driven models for optimal building operations, but there is a marked scarcity of studies on the parametric translation of design-oriented white-box models to thermal RC networks at the conceptual stage of building energy prediction. This paper presents a link between white- and grey-box modelling approaches for early design stage energy assessment of residential buildings by (i) developing multiple white-box building models through parametric modifications of window-to-wall ratio (WWR), total floor area and envelope properties (ii) reducing complexities of multi-scenario simulations into grey-box models and (iii) investigating the sensitivity of the model parameters to the simulated scenarios. For the parametric modifications of floor areas between 150 to 300 m² (1615 to 3229ft2), WWR of 20% to 60%, and envelope gypsum boards of 1 to 4 layers, the results yielded (i) Effective air capacitance ranges from 1x107 to 1.4x107J/K (9.5x103 to 1.3x104BTU/°F) and (ii) Envelope capacitance values between 2.2x107J/K to 3x107J/K (2 .1x104 to 2 .8x104BTU/°F) These findings extend the domain knowledge for quantifying the initial parameter values of thermal capacitances in the residential building stock. This study also presents a clustering approach to designers and energy system operators for multi-criteria design and control decisions with reduced computational overhead in aggregate energy prediction at the early design stage.
| File Size : | 1
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| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 9 |
| Product Code(s) : | D-CH-24-C122 |
| Published : | 2024 |
