Date of Award
Master of Science in Engineering Management
Department of Systems Engineering and Management
Justin D. Delorit, PhD
Climate variability is an external and stochastic factor that causes energy demand uncertainty. Energy managers can use climate-based models to understand future trends of energy demand and to adjust operations, policy, and budgets accordingly. This research focuses on 1) identifying how climate attributes impact energy use, 2) creating a historically informed statistical modeling framework to skillfully predict energy use, and 3) forecasting future changes to energy use and costs, using CMIP5 temperature projections, at the campus level. After synthesizing the existing breadth of research on climate-informed energy modeling, a skillful, unbiased, climate-informed total energy consumption prediction model is developed for Wright-Patterson AFB (WPAFB) that is particularly skillful at predicting energy use during high and low use periods: the periods where impactful energy policy decisions are made (r2= 73%, MAPE = 6.15, RPSS = 0.59). CMIP5 projections of temperature inform the model to generate energy use forecasts, which reveal significant changes to energy use within the next decade and increases in annual energy use costs by $7.3-7.9M by the end of the century. Overall, energy use predictions and forecasts can pinpoint the impact of climate factors, inform how and when to mitigate changes, and justify intervention timing and financial decisions.
DTIC Accession Number
Weiss, Scott C., "Climate-informed Prediction and Forecast Modeling of Installation Total Energy Consumption" (2021). Theses and Dissertations. 5012.