Dissertation Defense: Matthew Dahlhausen



  • Professor Jelena Srebric, Ph.D., Chair
  • Professor Reinhard Radermacher, Ph.D.
  • Professor Yunho Hwang, Ph.D.
  • Professor Bao Yang, Ph.D.
  • Professor Donald Milton, MD, DrPH, Dean’s Representative

Date/Time: Friday, August 28th 2:00-4:00pm EDT

Energy retrofits of existing buildings reduce grid requirements for new generation and reduce greenhouse gas emissions.  However, it is difficult to estimate energy savings both at the individual building and entire building stock level because building energy models are poorly calibrated to actual building performance.  This uncertainty has made it difficult to prioritize research & development and incentive programs for building technologies at the utility, state, and federal level.  This research seeks to make it easier to generate building energy models for existing buildings, and to calibrate buildings at the stock level to create accurate commercial building load forecasts.  Once calibrated, these building models can be used as seeds to other building energy model calibration approaches and to help utility, state, and federal actors to identify promising energy savings technologies in commercial buildings.  This research details the economics of a building energy retrofit at a singular building, contributes significantly to the development of ComStock, a model of the commercial building stock in the U.S., identifies important parameters for calibrating ComStock, and calibrates ComStock for an example utility region of Fort Collins, CO against individual commercial building interval data.  A study of retrofit costs finds that measure cost and model uncertainty are the most significant sources of variation in retrofit financial performance, followed by capital cost.  A wide range of greenhouse gas pricing scenarios shows they have little impact on the financial performance of whole building retrofits.  A sensitivity analysis of ComStock model inputs across an exhaustive range of models identifies 19 parameters that explain 80% of energy use and 25 parameters explain 90% of energy use.  Building floor area alone explains 41% of energy use.  Finally, a comparison of ComStock to Fort Collins, CO interval meter data shows a -12.6% normalized mean bias error and 23.5% coefficient of variation of root mean square error.  Improvements in meter classification and ComStock model variability will further improve model fit and provide an accurate means of modeling the commercial building stock.