  As a model-free optimization and decision-making method, deep reinforcement
learning (DRL) has been widely applied to the filed of energy management in
energy Internet. While, some DRL-based energy management schemes also
incorporate the prediction module used by the traditional model-based methods,
which seems to be unnecessary and even adverse. In this work, we implement the
standard energy management scheme with prediction using supervised learning and
DRL, and the counterpart without prediction using end-to-end DRL. Then, these
two schemes are compared in the unified energy management framework. The
simulation results demonstrate that the energy management scheme without
prediction is superior over the scheme with prediction. This work intends to
rectify the misuse of DRL methods in the field of energy management.
