The applicability of Lumped Parameter modelling in houses using in-situ measurements

The Lumped Parameter technique is a simplified thermal modelling method, which can be informed by measured operational data whilst allowing for physical interpretation of components and processes. The application of Lumped Parameter methods to the residential sector has been limited, primarily due t...

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Bibliographic Details
Main Authors: Vanda Dimitriou, Steven Firth, Tarek Hassan, Tom Kane
Format: Article
Language:English
Subjects:
Building & Construction
Built Environment and Design
Domestic buildings
Engineering
grey-box modelling
hybrid modelling
In-situ data
lumped parameter models
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Summary:The Lumped Parameter technique is a simplified thermal modelling method, which can be informed by measured operational data whilst allowing for physical interpretation of components and processes. The application of Lumped Parameter methods to the residential sector has been limited, primarily due to the lack of availability of operational data and the complex nature of real-world factors, such as variable occupant behaviours. This work investigates the potential of using the Lumped Parameter technique for modelling the performance of existing houses using high resolution in-situ measurements. Lumped Parameter models are created based on building surveys of eleven existing domestic buildings in the UK. The models are shown to have an average Root Mean Square Error (RMSE) of 1.35oC when compared to measured values of internal air temperature. A parametric analysis of model parameters showed wall thermal resistance, window area, boiler efficiency and infiltration rate as the most significant factors affecting the indoor air temperature calculated values. Model calibration improved the average RMSE to 1.03oC (a 23.7% decrease from initial fit). In conclusion, the Lumped Parameter models were able to realistically represent the majority of the houses, highlighting the technique’s potential in informing strategic retrofit decision making across the housing stock.