Building energy modeling has become more widely used for building performance assessment and building regulations compliance. The energy model of the SinBerBEST Cyber-Physical Testbed provides a straightforward “virtual testbed” platform for evaluating the performance, efficiency, and effectiveness of technological innovations.
However, before using for technological assessments, the testbed model itself should be calibrated using in-situ experiments from various operating conditions to ensure it reasonably follows the testbed’s real thermal and energy performance. Besides, the dataset derived from the calibration experiments will form a high-quality open empirical dataset that is rare in current model calibration research. The available datasets would be ideal as a benchmark dataset for assessing the predictive accuracy of different calibration algorithms.
The main objectives of this experiment are:
To calibrate the SinBerBEST Testbed EnergyPlus model using in-situ experiments
To obtain high-quality experimental datasets for building energy model validation/calibration of thermal and energy performance.
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. It was funded through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore.