  In this paper, statistical machine learning algorithms, as well as deep
neural networks, are used to predict the values of the price gap between
day-ahead and real-time electricity markets. Several exogenous features are
collected and impacts of these features are examined to capture the best
relations between the features and the target variable. Ensemble learning
algorithm namely the Random Forest issued to calculate the probability
distribution of the predicted electricity prices for day-ahead and real-time
markets. Long-Short-Term-Memory (LSTM) is utilized to capture long term
dependencies in predicting direct gap values between mentioned markets and the
benefits of directly predicting the gap price rather than subtracting the
predictions of day-ahead and real-time markets are illustrated. Case studies
are implemented on the California Independent System Operator (CAISO)
electricity market data for a two years period. The proposed methods are
evaluated and neural networks showed promising results in predicting the exact
values of the gap.
