Badr Al Tarhuni, Adel Ali Mohamed Naji



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Many U.S. utilities incentivize residential energy reduction through rebates, often in response to state mandates relative to energy reduction or from a desire to reduce demand in order to mitigate the need to grow generating assets or simply from a desire to provide service to customers. The assumption built into incentive programs is that the least efficient of residences will more likely take advantage of the rebates. This isn’t however always the case. The objective of this study is to show the potential for prioritized incentivization, e.g., incentivization that delivers the greatest energy savings per investment. The key question addressed in this research is “How can energy reduction measures be prioritized among all possible measures for all residences in an entire customer base to yield the greatest savings per investment?” A data based approach leveraging knowable or potentially knowable building characteristics (wall, ceiling, and window R-values, heating and water heating system efficiencies, floor area, window area) and energy characteristics (annual weather normalized heating and water heating energy consumption) is used to estimate natural gas savings from the most important measures for all houses within a utility district. This approach relies upon the establishment of a single data-based model to accurately predict energy consumption of the collective grouping of houses. Using this model energy savings and costs from all possible measures can be predicted. This approach enables the possibility of sequential adoption of the most cost effective energy measures. The specific case considered addresses hundreds of student residences owned a university in the U.S. Midwest. The results show that an energy (carbon) reduction of 36% can be achieved with this methodology at a levelized cost of less than $14/mmBTU.

Publication Date


Project Designation

Graduate Research - Graduate

Primary Advisor

Kevin P. Hallinan

Primary Advisor's Department

Mechanical and Aerospace Engineering


Stander Symposium project

Data-Based Approach for Most Cost Effective Residential Energy Reduction