  The demand response (DR) program of a traditional HEMS usually intervenes
appliances by controlling or scheduling them to achieve multiple objectives
such as minimizing energy cost and maximizing user comfort. In this study,
instead of intervening appliances and changing resident behavior, our proposed
strategy for hour-ahead DR firstly learns appliance use behavior of residents
and then silently controls ESS and RES to minimize daily energy cost based on
its knowledge. To accomplish the goal, our proposed deep neural networks (DNNs)
models approximate MILP optimization by using supervised learning. The datasets
for training DNNs are created from optimal outputs of a MILP solver with
historical data. After training, at each time slot, these DNNs are used to
control ESS and RES with real-time data of the surrounding environment. For
comparison, we develop two different strategies named multi-agent reinforcement
learning-based strategy, a kind of hour-ahead strategy and forecast-based MILP
strategy, a kind of day-ahead strategy. For evaluation and verification, our
proposed strategies are applied at three different real-world homes with
real-world real-time global horizontal irradiation and real-world real-time
prices. Numerical results verify that the proposed MILP-based supervised
learning strategy is effective in term of daily energy cost and is the best one
among three proposed strategies
