Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets

Abstract

The ability to forecast real-time electricity price for wind power is key to the operation of energy markets and hedging price risks. Recent research suggests new deep neural network (DNN) architectures can capture temporal dependencies in historical price data, along with the ability to automatically extract important features of the dataset. However, most existing price prediction DNN representations still utilize basic architecture designs and either no pre-training, or simple training approaches. This work studies both the effect of transfer learning on three network representations and different source domains, as well as the mechanism of transfer learning. It is shown that transfer learning improves accuracy across all network representations. The best performance is obtained with a GRU-based architecture, termed GRU-TL, that has been pre-trained from a hybrid dataset of all wind farms in the same subzone. This model outperforms all statistical and deep learning benchmarks by an average of 6.7% in the mean absolute percent error (MAPE) metric. The underlying mechanism of transfer learning enables the pre-trained DNN representation to learn the features of the target dataset more accurately.