Preprint Article Version 1 This version is not peer-reviewed

Electricity Price Forecasting Using Recurrent Neural Networks

Version 1 : Received: 20 April 2018 / Approved: 23 April 2018 / Online: 23 April 2018 (11:38:27 UTC)

A peer-reviewed article of this Preprint also exists.

Ugurlu, U.; Oksuz, I.; Tas, O. Electricity Price Forecasting Using Recurrent Neural Networks. Energies 2018, 11, 1255. Ugurlu, U.; Oksuz, I.; Tas, O. Electricity Price Forecasting Using Recurrent Neural Networks. Energies 2018, 11, 1255.

Journal reference: Energies 2018, 11, 1255
DOI: 10.3390/en11051255

Abstract

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not 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 and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-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.

Subject Areas

electricity price forecasting; deep learning; gated recurrent units; long short term memory; artificial intelligence, turkish day-ahead market

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