On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

2019 ◽  
Vol 35 (4) ◽  
pp. 1520-1532 ◽  
Author(s):  
Grzegorz Marcjasz ◽  
Bartosz Uniejewski ◽  
Rafał Weron
Keyword(s):  
Neural Networks ◽  
Electricity Price ◽  
Price Forecasting ◽  
Seasonal Component ◽  
Electricity Price Forecasting

scholarly journals Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3249
Author(s):  
Arkadiusz Jędrzejewski ◽  
Grzegorz Marcjasz ◽  
Rafał Weron
Keyword(s):  
Neural Network ◽  
Model Calibration ◽  
Electricity Price ◽  
Price Forecasting ◽  
Seasonal Component ◽  
Spot Prices ◽  
Electricity Price Forecasting ◽  
Selection Operator ◽  
Seasonal Decomposition

Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

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.

Data-Driven Modeling for Long-Term Electricity Price Forecasting

2021 ◽  
Author(s):  
Paolo Gabrielli ◽  
Paolo Gabrielli ◽  
Steffen Blume ◽  
Giovanni Sansavini
Keyword(s):  
Electricity Price ◽  
Data Driven ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
Data Driven Modeling

Electricity price forecasting using neural networks with an improved iterative training algorithm

2017 ◽  
Vol 39 (2) ◽  
pp. 147-158 ◽  
Author(s):  
Morteza Gholipour Khajeh ◽  
Akbar Maleki ◽  
Marc A. Rosen ◽  
Mohammad H. Ahmadi
Keyword(s):  
Neural Networks ◽  
Electricity Price ◽  
Price Forecasting ◽  
Training Algorithm ◽  
Electricity Price Forecasting
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