Electricity Price Forecasting Using Recurrent Neural Networks - Ugurlu, Umut; Oksuz, Ilkay; Tas, Oktay; Öksüz, İlkay (MDPI AG, 2018)
Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market.
Keywords: electricity price forecasting; deep learning; gated recurrent units; long short term memory; artificial intelligence; turkish day-ahead market; Artificial intelligence; Electricity price forecasting; finance; Deep learning; Gated recurrent units; Turkish day-ahead market; Long short term memory