provided on electrical energy use at the appliance or individual lighting circuit level. A number of possibilities arise as a result, including detailed eco-feedback about usage and tighter coupling with smart grid technology on the supply side. Similar feedback is possible for other resources such as water and natural gas.

This possibility does, however, raise a number of challenging research questions. For example, what is the appropriate amount of information to provide to households? Clearly there is the possibility of overwhelming them with information. How are the inherent uncertainties in the data to be dealt with? How are such systems to be evaluated? The traditional HCI evaluation techniques of laboratory studies and small-scale deployments are inadequate, but massive deployments over long periods are slow and expensive, implying that one can only try a small number of alternatives (in tension with the need for rapid prototyping and iteration). How can these systems be coupled with smart grid technology on the supply side? For example, the grid could signal to the household that the system was close to capacity and that lowering energy use for the next hour would be very helpful (or perhaps would result in a lower bill); or, conversely, the household could be signaled that this would be an opportunity for some non-time-critical activity. This arrangement would be a combination of automated actions, with the scripts under the household’s control, and explicit actions.

Another set of issues concerns fairness and representativeness. For example, the majority of households in the United States are low-income and many households rent, although most work in this area focuses on relatively affluent homeowners. Can systems and policies be designed that do not unfairly disadvantage some households, particularly ones that can least afford additional charges? Another set of challenges concerns security and privacy. Such systems offer the potential for reducing resource consumption and making better use of resources, but there are clear security and privacy risks if the system is compromised. Related to that issue are questions of responsibility and power around available infrastructure that must be addressed. Not everyone owns a home or pays for energy use, and the relationships between landlords, residents, laws (incentives, disincentives, and so on), available services (green contractors), and other factors influence energy use outcomes and may bear on the design of technology (for example, in terms of authenticating who has access to what data).

It is difficult to get good information about the fine-grained use of energy right now. Buildings are not generally instrumented to produce these data, yet a true understanding of the forces driving energy use is impossible without better data. Better information about which appliances are in use and when they are in use can help in developing a more complete

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