Improving the energy model calibration process in the context of performance gap, using energy and environmental data
This PhD addresses the pressing challenge of making energy models of existing buildings more accurate by reliably calibrating them with monitored data, so that they can be used for identification and mitigation of performance gap issues. The performance gap issues are not just limited to energy but also include Indoor Environment Quality (IEQ) parameters such as Thermal Comfort and Indoor Air Quality. In this study, model calibration is used to identify and validate the root causes of performance gaps in five UK case study buildings across the building sector. During this process various issues related to model calibrations and validation are addressed, such as, the relationship of calibrated model validation methods with data availability. Additionally, calibration accuracy and data granularity; and the importance of IEQ data in calibration as an input parameter as well as calibration objective are also explored.
The research while deepening the understanding about root causes of performance gap across the building sector, advances the current energy model practices. It pushes towards an evidence-based calibration approach to identification of building performance issues. The advancements proposed to the current calibration practices such as improved validation methods and minimum IEQ verification would enable development of more robust and useful calibrated models.
Financial support for various aspects of the work has been provided by UCL Overseas Research Scholarships (UCL-ORS), UCL Centre in Virtual Environments Interaction and Visualisation (VEIV), DesignBuilder Software Ltd. and the 'Total Performance' of Low Carbon Buildings in China and the UK ('TOP') project funded by EPSRC (EP/N009703/1).