  A study on power market price forecasting by deep learning is presented. As
one of the most successful deep learning frameworks, the LSTM (Long short-term
memory) neural network is utilized. The hourly prices data from the New England
and PJM day-ahead markets are used in this study. First, a LSTM network is
formulated and trained. Then the raw input and output data are preprocessed by
unit scaling, and the trained network is tested on the real price data under
different input lengths, forecasting horizons and data sizes. Its performance
is also compared with other existing methods. The forecasted results
demonstrate that, the LSTM deep neural network can outperform the others under
different application settings in this problem.
