The goal of this project is to investigate the propagation of uncertainty in a knowledge-based system that assesses energy management strategies for new gas and electric technologies that can help reduce energy consumption and demand. The new technologies that have been investigated include lighting, electric heat pumps, motors, refrigerators, microwave clothes dryers, freeze concentration, electric vehicles, gas furnaces, gas heat pumps, engine-driven chillers, absorption chillers, and natural gas vehicles distributed throughout the residential, commercial, industrial, and transportation sectors;The description of a complex assessment system may be simplified by allowing some degree of uncertainty. A number of uncertainty-representing mechanisms, such as probability theory, certainty factors, Dempster-Shafer theory, fuzzy logic, rough sets, non-numerical methods, and belief networks, were reviewed and compared. The proper application of uncertainty provides an effective and efficient way to represent knowledge;A knowledge-based system has been developed to assess the impacts of rebate programs on customer adoption of new technologies and, hence, the reductions in energy and demand. Three modes have been programmed: (1) one in which uncertainty is not considered, (2) another where fuzzy logic with linguistic variables is used to represent uncertainty, and (3) one in which uncertainty is represented using Dempster-Shafer theory with basic probability assignments. A correlation for rebate, expected (energy) savings, and customer adoption is employed in the knowledge base. Predictions for annual adoption of a new technology are made for specified useful life, rebate, and expected savings; or a suggested rebate can be determined for specified useful life, expected savings, and annual adoption. With input for energy use and demand for each technology, the impacts of rebate programs on energy use and power demand can be evaluated;This report and the knowledge-based system should help utilities determine these new technologies that are most promising and these strategies that should be emphasized in their energy management programs.