@article{35824, keywords = {Uncertainties, Model calibration, University campus, Urban building energy model, Energy retrofit}, author = {Ziqi Lin and Tianzhen Hong and Xiaodong Xu and Jiayu Chen and Wei Wang}, title = {Evaluating energy retrofits of historic buildings in a university campus using an urban building energy model that considers uncertainties}, abstract = {

Urban building energy model (UBEM) is a powerful tool to simulate performance and evaluate efficiency of upgrades for a group of buildings under the urban context. However, the larger the scale/number of buildings, more parameters must be collected to create energy models that cover each individual building, causing more uncertainties. To reveal this, this study created a UBEM for the mixed modern and historic buildings at a campus in China. The calibrated set of UBEMs with the modeled results meeting the 20% error range, were then used to evaluate uncertainties of energy-savings of four building energy retrofit (BER) measures. The first measure, BER 1, was to preserve the historic values of buildings; BER 2 to meet green building design standard; BER 3 to achieve 20% more savings than BER 2; and BER 4 to utilize renewable photovoltaic energy. For BER 1, BER 2, and BER 3, the energy savings of buildings of different ages varied within 10%–44%. For BER 4, the energy savings of buildings varied within 49%–505%, respectively, where the reason for higher than 100% is energy production is much higher than energy demand. Similar results can be concluded for building functions, for BER 1, BER 2, and BER 3, the energy-saving potentials varied within 6%– 45%, while 97%–492% to BER 4. This study can provide an important and significant reference to apply UBEM in evaluating energy-efficient retrofits as well as other energy-related studies that consider uncertainties.

}, year = {2023}, journal = {Sustainable Cities and Society}, volume = {95}, pages = {104602}, month = {04/2023}, issn = {22106707}, url = {https://linkinghub.elsevier.com/retrieve/pii/S2210670723002135}, doi = {10.1016/j.scs.2023.104602}, language = {eng}, }