Day-Ahead Price Forecasting for Electricity Market using Long-Short Term Memory Recurrent Neural Network

In this paper, an efficient method for the day-ahead electricity price forecasting (EPF) is proposed based on a long-short term memory (LSTM) recurrent neural network model. LSTM network has been widely used in various applications such as natural language processing and time series analysis. It is capable of learning features and long term dependencies of the historical information on the current predictions for sequential data. We propose to use LSTM model to forecast the day-ahead electricity price for Australian market at Victoria (VIC) region and Singapore market. Instead of using only historical prices as inputs to the model, we also consider exogenous variables, such as holidays, day of the week, hour of the day, weather conditions, oil prices and historical price/demand, etc. The output is the electricity price for the next hour. The future 24 hours of prices are forecasted in a recursive manner. The mean absolute percentage error (MAPE) of four weeks for each season in VIC and Singapore markets are examined. The effectiveness of the proposed method is verified using real market data from both markets. The result shows that the LSTM network outperforms four popular forecasting methods and provides up to 47.3% improvement in the average daily MAPE for the VIC market.