To assess energy performance of building complexes, researchers at Cornell University designed an energy model, which includes data obtained with greenTEG’s U-Value Kit. The goal of the research was to design partially automated energy models which provide detailed building performance metrics to efficiently manage future energy demand.

The full publication is available here.

Towards Automated Model Generation and Calibration to Facilitate Multi Building Scale Energy Modeling

Proceedings of eSim 2018, the 10ᵗʰ conference of IBPSA-Canada Montréal, QC, Canada, May 9-10, 2018

Authors: Eesha Khanna, Allison Bernett, Timur Dogan

Abstract: As negative effects of climate change become increasingly prevalent, carbon emission reduction has become the need of the hour. To meet carbon reduction goals, municipalities, universities and organizations with large real estate portfolios need reliable and adaptable models that provide detailed building performance metrics to efficiently manage future energy demand. However, creating calibrated energy models at the multi-building scale is often a time-consuming task. This paper, hence, investigates methodologies to partially automate the buildup and calibration of energy models for the authors’ home institution. This paper presents a workflow that uses institutional GIS datasets, metered energy use and quick surveys as inputs to generate multi-zone EnergyPlus building energy models that are then calibrated using parameter screening and optimization. The calibrated models are used to assess energy performance under the projected climate in the future and evaluate retrofitting scenarios. Accuracy and applicability of the methodology are demonstrated for one campus building and results show that models can be generated with feasible effort reflect satisfactory accuracy for annual, monthly and daily resolutions.

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