  A smart home energy management system (HEMS) can contribute towards reducing
the energy costs of customers; however, HEMS suffers from uncertainty in both
energy generation and consumption patterns. In this paper, we propose a
sequence to sequence (Seq2Seq) learning-based supply and load prediction along
with reinforcement learning-based HEMS control. We investigate how the
prediction method affects the HEMS operation. First, we use Seq2Seq learning to
predict photovoltaic (PV) power and home devices' load. We then apply
Q-learning for offline optimization of HEMS based on the prediction results.
Finally, we test the online performance of the trained Q-learning scheme with
actual PV and load data. The Seq2Seq learning is compared with VARMA, SVR, and
LSTM in both prediction and operation levels. The simulation results show that
Seq2Seq performs better with a lower prediction error and online operation
performance.
