Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models
In day-ahead electricity price forecasting the daily and weekly seasonalities are always taken into account, but the long-term seasonal component was believed to add unnecessary complexity and in most studies ignored. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling f...
| Main Authors: | , , |
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| Format: | Report |
| Language: | unknown |
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| Online Access: | http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_17_03.pdf |
| Summary: | In day-ahead electricity price forecasting the daily and weekly seasonalities are always taken into account, but the long-term seasonal component was believed to add unnecessary complexity and in most studies ignored. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, the latter is based on linear models estimated using Ordinary Least Squares. Here we show that considering non-linear neural network-type models with the same inputs as the corresponding SCAR model can lead to a yet better performance. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can significantly outperform the latter. Electricity spot price; Forecasting; Day-ahead market; Long-term seasonal component; Neural network; Committee machine |
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