The U.S. Energy Information Administration estimates that 41% of the total national energy consumption is related to buildings. Due to the relatively long lifespan of buildings, many operate inefficiently. Unfortunately most homeowners lack the knowledge and skills to identify and rectify these inefficiencies. Even when inefficiencies are identified it is extremely difficult for a homeowner to identify cost-effective solutions.
Develop an easy-to-use web-based household energy efficiency web application able to quantify the effects of various energy efficiency measures in a way understandable to the homeowner. The website would collect building information such as location, square footage, construction style, insulation type, appliances and heating/cooling equipment. This information would be fed into existing building energy models (suggest NREL BEopt and/or DOE EnergyPlus) to estimate annual energy consumption. Using an existing database of energy efficiency measures (from NREL), the simulation would be iteratively recomputed to quantify the effects of each measure. Finally, using the building data, a payback period would be computed and the most effective measures would be displayed to the homeowner in a visually appealing manner.
Potentially reduce national energy consumption and reduce global warming.
None at this time.
Access to NREL scientists and many experts in the field.
Closed source using open-source tools. Owned by the students/Bucknell.
Point of Contact
Prof. Alan Marchiori, Department of Computer Science