GROUP Badr Al Tarhuni, Adel Ali Mohamed Naji



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Cost effective retrofits of residential buildings could yield annual electricity savings in this sector of approximately 30 percent in the United States. Furthermore, investment in energy efficiency can create millions of direct and indirect jobs throughout the economy for manufacturers and service providers that supply the building industry. Unfortunately, the actual energy savings, compared to predictions based upon physical energy models, have been somewhat disappointing, leading to wariness on the part of those wishing to invest in efficiency projects. The objective of this study is to use an expanded set of building characteristic data to predict savings from the adoption of individual energy saving measures based upon actual building data and not only on energy models. Key to this study will be the use of a large number of buildings/residences for which all energy characteristics are known. The specific case considered here involves hundreds of university-owned student residences in the U.S. Midwest. A neural network approach is used to develop a single model that accurately predicts heating energy for all houses given the specified energy characteristics. The resulting neural net is used to predict savings associated with a small subset of houses in the study which have already been upgraded from a variety of measures. The estimated savings are compared to the actual savings realized. The results show that the predicted savings match the actual savings within 2.5 percent for most of the measures considered. These results show the potential for establishing larger public databases of building energy characteristics in order to strategically implement energy reduction strategies with the greatest energy savings per cost to implement

Publication Date


Project Designation

Graduate Research - Graduate

Primary Advisor

Kevin P. Hallinan

Primary Advisor's Department

Mechanical and Aerospace Engineering


Stander Symposium project

Predicting Residential Heating Energy Consumption and Savings from Known Energy Characteristics and Historical Energy Consumption