Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

Özgüner, Erdem; Tör, Osman Bülent; Güven, Ali

doi:10.3906/elk-1612-206

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…However, overall, ANN model is a little better than the ARIMA model. In another work, Ozguner et al [51] proposed an ANN model to forecast the hourly electricity prices and loads in the Turkish market and compared the results with multiple linear regression. Findings of this paper is very similar to [50]; in both papers, ANN model outperforms ARIMA model with a small difference.…”

Section: Turkish Marketmentioning

confidence: 99%

Electricity Price Forecasting Using Recurrent Neural Networks

2018

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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.

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Section: Turkish Marketmentioning

confidence: 99%

Electricity Price Forecasting Using Recurrent Neural Networks

2018

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show abstract

“…But, overall, ANN model is a little better than the ARIMA model. In another work, Ozguner et al [50] propose an ANN model to forecast the hourly electricity prices and loads in the Turkish market and compare the results with multiple linear regression. Findings of this paper is very similar to [49]; in both papers, ANN model outperforms ARIMA model with a small difference.…”

Section: Turkish Marketmentioning

confidence: 99%

Electricity Price Forecasting Using Recurrent Neural Networks

Uğurlu¹,

Öksüz²,

Taş³

2018

Preprint

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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.

show abstract