  Demand Response (DR) schemes are effective tools to maintain a dynamic
balance in energy markets with higher integration of fluctuating renewable
energy sources. DR schemes can be used to harness residential devices'
flexibility and to utilize it to achieve social and financial objectives.
However, existing DR schemes suffer from low user participation as they fail at
taking into account the users' requirements. First, DR schemes are highly
demanding for the users, as users need to provide direct information, e.g. via
surveys, on their energy consumption preferences. Second, the user utility
models based on these surveys are hard-coded and do not adapt over time. Third,
the existing scheduling techniques require the users to input their energy
requirements on a daily basis. As an alternative, this paper proposes a DR
scheme for user-oriented direct load-control of residential appliances
operations. Instead of relying on user surveys to evaluate the user utility, we
propose an online data-driven approach for estimating user utility functions,
purely based on available load consumption data, that adaptively models the
users' preference over time. Our scheme is based on a day-ahead scheduling
technique that transparently prescribes the users with optimal device operation
schedules that take into account both financial benefits and user-perceived
quality of service. To model day-ahead user energy demand and flexibility, we
propose a probabilistic approach for generating flexibility models under
uncertainty. Results on both real-world and simulated datasets show that our DR
scheme can provide significant financial benefits while preserving the
user-perceived quality of service.
