  Electricity price is a key factor affecting the decision-making for all
market participants. Accurate forecasting of electricity prices is very
important and is also very challenging since electricity price is highly
volatile due to various factors. This paper proposes an integrated long-term
recurrent convolutional network (ILRCN) model to predict electricity prices
considering the majority contributing attributes to the market price as input.
The proposed ILRCN model combines the functionalities of convolutional neural
network and long short-term memory (LSTM) algorithm along with the proposed
novel conditional error correction term. The combined ILRCN model can identify
the linear and non-linear behavior within the input data. We have used ERCOT
wholesale market price data along with load profile, temperature, and other
factors for the Houston region to illustrate the proposed model. The
performance of the proposed ILRCN electricity price forecasting model is
verified using performance/evaluation metrics like mean absolute error and
accuracy. Case studies reveal that the proposed ILRCN model is accurate and
efficient in electricity price forecasting as compared to the support vector
machine (SVM) model, fully-connected neural network model, LSTM model and the
LRCN model without the conditional error correction stage.
