Search

Most demand response programs focus primarily on commercial and industrial loads. The sheer number of decision-influencing variables combined with a lack of a comprehensive understanding of human behavior make it difficult to develop response programs that encourage greater residential consumer participation. This paper presents a multi-criteria decision making approach using actual survey data for identifying user preferences. The novelty of the work is in considering functionality, cost, and carbon emissions for a given set of home appliances toward energy management. The paper explains the design methodologies used to prioritize the preferences from 1023 survey participants. The results are expected to inform the design of Home Energy Management Systems (HEMS) for participating in demand response programs.

One challenge of transitioning to renewable energy is that household electricity use and renewable generation are often misaligned. Smart home energy management systems hold promise for shifting usage to match generation, but these systems need to be designed with the occupants’ preferences in mind. The purpose of the present research is to compare two approaches for collecting and modeling consumers’ load management preferences, both of which are amenable to use in a home energy management system. Specifically, we examine the performance of Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Analytic Hierarchy Process (AHP) in quantifying consumers’ preferences regarding air temperature (air conditioning and heating), water heating, dishwashing, clothes washing and drying, monetary costs, environmental impacts, and comfort/convenience. Two studies are presented: Study 1 examines the SMARTER approach, and Study 2 focuses on the AHP approach. In both studies, online surveys (NSMARTER = 956 and NAHP = 1023) were conducted to elicit preferences from participants across the United States. The preferences modeled by both approaches were validated based on (a) their ability to predict participants’ choices in a Discrete Choice Experiment and (b) their convergence with previous research on load-shifting behavior. The validation procedure suggests that the SMARTER approach is superior in modeling consumers’ preferences for load management. This research lays the groundwork for designing a smart home interface capable of collecting occupants’ preferences and using those preferences to deliver improved occupant comfort, lower operating costs, reduced environmental impact, and more significant demand response than exists today.