Transfer Learning for Electricity Price Forecasting

The task has been studied in different markets separately and learning interdependent information in between different markets is an understudied field. Recently, deep learning methods have showcased superior performance in predicting electricity prices [1]. In particular, recurrent neural networks have been able to learn sequential information in time-series type data sets [2]. Most of the the literature on the application of neural networks for electricity price forecasting has relied on single market data and available large amounts of data from different markets have not been utilized. Transfer Learning is a major tool to improve the performance on image classification problems. The networks can be trained on similar problems before finally being trained on the final problem to leverage from the data to the fullest. In this paper, we utilize the concept of transfer learning for electricity price forecasting by using data from five different markets. Our major novelties can be listed as: 1. We investigate the different ways to combine data from different elec-2 tricity markets, when training neural networks, 2. We propose the transfer learning scheme to leverage from different market data, when training recurrent neural networks (RNN) for the task of price prediction.