Forecasting and Modelling of Electricity Prices by Radial Basis Functions: Turkish Electricity Market Experiment

Özyildirim, Cenktan; Beyazit, Mehmet Fuat

doi:10.3848/iif.2014.344.4256

Cited by 8 publications

(5 citation statements)

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“…Findings of this paper is very similar to [50]; in both papers, ANN model outperforms ARIMA model with a small difference. Ozyildirim and Beyazit [52] compared another machine learning method, radial basis function, with the multiple linear regression. In their work, difference between the prediction performance of the models are negligible.…”

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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“…Findings of this paper is very similar to [49]; in both papers, ANN model outperforms ARIMA model with a small difference. Ozyildirim and Beyazit [51] compare another machine learning method, radial basis function, with the multiple linear regression. In their work, difference between the prediction performance of the models are negligible.…”

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.

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“…Trade in the balancing/intraday market can be done until a few hours before the delivery. For instance, in the developed German EPEX intraday market, trade continues until just 15 min before the delivery [24][25][26]. In the Turkish intraday market, electricity is tradeable from 18:00 of the previous day until one hour before the delivery.…”

Section: Intraday Electricity Marketmentioning

confidence: 99%

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Öksüz

Uğurlu

2019

Energies

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The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

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Forecasting and Modelling of Electricity Prices by Radial Basis Functions: Turkish Electricity Market Experiment

Cited by 8 publications

References 0 publications

Electricity Price Forecasting Using Recurrent Neural Networks

Electricity Price Forecasting Using Recurrent Neural Networks

Electricity Price Forecasting Using Recurrent Neural Networks

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

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