Information Framework for Intelligent Decision Support System for Home Energy Retrofits

Abstract

Residential buildings are one of the major consumers of energy. The majority of the housing stock consists of existing homes, and a large number of these homes are energy inefficient. Retrofitting existing homes to make them energy efficient can contribute immensely to energy savings. Buildings in the United States consume 72% of electricity and 40% of all energy, and residential buildings account for over half of these percentages. Existing buildings consume more than 70% of the electricity consumed by the buildings. With more than 130 million existing homes, energy retrofitting of existing homes is a critical area. Literature has identified that a key impediment to home energy retrofits (HER) is the lack of information or the lack of information in a format that the homeowners can understand and use to make retrofit decisions. Homeowners traditionally depend on experts in the field of energy efficiency, such as energy auditors and trade contractors, for information. There are, however, several problems with such information, including comprehensiveness, inaccuracy, cost, and perception of bias. The proposed work contributes toward understanding and solving the information barriers in the adoption of energy retrofits in existing homes. Its primary contribution lies in developing an information model for the energy retrofit decision process that can serve as a basis for the future development of a fully functional intelligent decision support system (IDSS). The IDSS will integrate quantitative information with expert knowledge in an interactive computer-based decision tool to assist the user. It will analyze the current situation of a home through queries to the user, and based on the quantitative information and expert knowledge in the system, provide customized and prioritized information needed for decision making by the user. The paper describes and categorizes various types of information needed for a retrofit decision, proposes a decision-making process model for HER, and developes a framework for organizing and integrating the quantitative information with the expert knowledge in an expert system-based IDSS. The quantitative information was gathered from published sources and from a U.S. Department of Energy’s cost database, and the expert knowledge was obtained by interviewing and job shadowing energy auditors and contractors. Finally, an example was provided to demonstrate the functioning of the proposed system. The example assists the user in identifying and prioritizing retrofit measures and provides expert advice on installation of selected measures.