  In the modern power market, electricity trading is an extremely competitive
industry. More accurate price forecast is crucial to help electricity producers
and traders make better decisions. In this paper, a novel method of
convolutional neural network (CNN) is proposed to rapidly provide hourly
forecasting in the energy market. To improve prediction accuracy, we divide the
annual electricity price data into four categories by seasons and conduct
training and forecasting for each category respectively. By comparing the
proposed method with other existing methods, we find that the proposed model
has achieved outstanding results, the mean absolute percentage error (MAPE) and
root mean square error (RMSE) for each category are about 5.5% and 3,
respectively.
