This research project is about understanding how to improve the efficiency of DR implementation in buildings and how to improve the accuracy of the electricity saving potential estimates of buildings that are participating in DR programs. We are approaching this problem by (1) examining a large pool of DR protocols in terms of the building information needed in each protocol and look at how building information models can help to streamline access to facility information; (2) automatically extracting synthesized DR related building information from existing BIMs; and (3) estimating the true saving potential of buildings by applying advanced data analysis techniques (e.g., machine learning, deep learning).
Yu, X., and Ergan, S. (2018). “BIM coverage in demand response management: a pilot study in campus buildings.” In Construction Research Congress 2018, pp. 316-325, April 2-4, 2018, New Orleans, LA, U.S.A. DOI: https://ascelibrary.org/doi/abs/10.1061/9780784481264.031
Yu, X., and Ergan, S. (2018). “A data-driven framework to estimate saving potential of buildings in demand response events.” In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, IAARC Publications, Vol. 35, pp. 1-8, July 20-25, 2018, Berlin, German. DOI: https://doi.org/10.22260/ISARC2018/0145
Yu, X., and Ergan, S. (2019). “Identification of principal factors in determining building peak energy shaving capacities during demand response events.” In The 2019 ASCE International Conference on Computing in Civil Engineering, June 17-19, 2019, Atlanta, GA, U.S.A.
